What are Industrial Data? Setting the stage for an emerging industry study

by Supraja Sudharsan, Jennifer Clark, Thomas Lodato

This blog post is a product of the industrial data and regional economic development study currently being undertaken by Dr. Jennifer Clark and the team at the Center For Urban Innovation (CUI) including Supraja Sudharsan and Dr. Thomas Lodato. The study is being conducted in collaboration with the team at the Energy Policy and Innovation Center (EPICenter), including Dr. Tim Lieuwen, Dr. Richard Simmons, and Kerri Metz. The objective of the study is to delineate the emerging industry around industrial data (ID), and unpack its regional clustering characteristics.


Digitalization of products and processes has ensured that all equipment, processes, and their interactions in both the physical and digital space generate data. The rising ability of firms to handle so-called “big data,” and to convert them into useful products, services, and product-embedded services, is projected to galvanize legacy industry sectors in advanced economies. This is increasingly referred to as a fourth industrial revolution.

The Industrial Data and Regional Economic Development Study undertaken by the team at CUI, in collaboration with EPICenter, affirms the criticality of data in industries. By proposing and applying a definition for “industrial data” (ID), the study reifies the emerging industry around ID. This blog post is based on the first phase of research, which was completed in September 2017.

What are industrial data?

Industrial data are data obtained by measuring and assessing the performance of an industrial equipment during production, and while in operation. Consider aviation manufacturing. Data are generated during the manufacture of jet engines, from the manufacture and supply of avionics, from tools and equipment during aircraft assembly, and while these equipment are in operation during flight. These data come from different parts of the world, based on the global foot print of aviation supply chain, and flight operations. The data come in a variety of formats including text, images, written records of historical maintenance information, and more. They are obtained by measuring performance parameters (such as temperature, pressure, vibration) of industrial equipment, at varying frequencies [1].

In general, ID includes:

1) Data from advanced manufacturing industries, such as aviation and automotive industries, including equipment manufacturers that operate in these industries,
2) Data from energy generation,
3) Data from energy transmission and (non-consumer) distribution data, and
4) Data from equipment in operation, such as wind turbines following installation.

The generalized definition of ID and its sectoral delineation helps us in three ways. First, it compiles existing models of ID across a wide range of initiatives enunciated by public, private and hybrid organizations globally. Second, it enables us to disaggregate the actors, processes, and their relationships to trace an emerging ID production chain. Third, it reveals that the processes involved in ID production are leading to the evolution of new firms and services around ID, and an emerging industry around ID itself.

Competing models and value propositions of ID

By defining ID as data obtained by measuring as well as assessing the performance of an industrial equipment, we consider the sources and uses of ID across different models of ID, and their value for a wide range of stakeholders including firms, industries, regions, and society (see Tables 1 and 2).

Table 1 shows how the German concept of Industrie 4.0 undertakes a process-oriented approach to industrial data. It considers ID as the consequence of a hierarchical process that is differentiated from the previous phases of industrial revolution via the presence of cyber-physical systems. Similarly, General Electric’s industrial internet definition refers to the “integration of physical machinery with networked sensors and software.” Other initiatives based on concepts such as advanced industry and smart manufacturing define their models by the impact of ID on the composition of high-skilled workers and by the influence of ID on the time to decision-making in business. Moreover, as seen in Table 2, the initiatives also vary in how they value ID for firms, industries, regions, and nation-states. One instance of this is GE’s industrial internet initiative, which estimates that the business value of ID will be $225 billion by 2020.

By undertaking a generalized definition of ID, we ensure that the study encompasses both the process and outcome-based approaches, as evidenced above. This in turn allows us to study the entire ID production, and delineate associated actors, and processes, as well as their outcomes.

Table 1. Leading models that study industrial data production (authors’ analysis)

Model name (Initiative)

Model definition


Authors’ analysis of model approach

Industrie 4.0 A fourth industrial revolution arising from the development of cyber-physical systems in manufacturing


Technology and process-focused: Refers to a hierarchical process in the evolution of manufacturing
Industrial Internet Refers to the integration of hardware, software, and communication technologies in industries


Technology-focused: Refers to connections between technologies
Advanced industry “An industry’s R&D spending per worker must fall in the 80th percentile of industries or higher, exceeding $450 per worker. The share of workers in an industry whose occupations require a high degree of STEM knowledge must also be above the national average, or 21 percent of all workers.”


Outcome-focused: Refers to composition of workers in industry
Advanced Manufacturing “Manufacturing that entails rapid transfer of science and technology (S&T) into manufacturing products and processes.”


Technology and process-focused
Smart Manufacturing “Smart Manufacturing is the ability to solve existing and future problems via an open infrastructure that allows solutions to be implemented at the speed of business while creating advantaged value.”


Outcome-focused: Refers to a business model

Table 2. Conceptions of value from industrial data (authors’ analysis)



Source of Value

Asset Performance Management – monitor operations, predict failure, optimize performance.

[3], [7]

Business Value

Prediction of power outage and communication of the same to residents.


Societal Value

Prediction of improvement in productivity and jobs.


Regional economic development

Projection of opportunities in advanced and developing countries -US and Europe, South Korea and China are expected to be early takers of industrial big data solutions.


Geographic value

Investment in industrial big data products and processes is highest in Aviation, followed by Wind, Manufacturing, Rail, Power generation, distribution, Oil and Gas, and Mining industry, in the order listed.


Sectoral value

Delineating ID production

Figure 1 gives a general overview of the production process based on data in power generation. Data are collected from power generation assets, such as gas and wind turbines, associated controls, and the surrounding environment. Through strategic partnerships between technology providers, utility, equipment providers and others, data from different sources are aggregated, and integrated across software systems for analysis. Finally, they are used to monitor, analyze, and visualize, present and future equipment performance. Consequently, firms perform four types of operations using collected data:

1) Monitor data real time or otherwise,
2) Diagnose equipment performance by comparing them to historical and baseline conditions,
3) Predict future performance based on this analysis/prescribe how to respond to predicted outcome, and
4) Optimize equipment performance based on predicted outcome.

Figure 1. Industrial Data Production, by authors

Tracing an emerging industry

Our analysis reveals that ID do not simply refer to digitalization of existing industries. The process of ID production, beginning from data collection to storage to analysis and use, are similar to other production processes. That is, similar to say, an automotive or textile production process, ID production has an input phase, the input then undergoes transformation, to be distributed and consumed by end-users. This leads us to the conclusion that the production process associated with ID is leading to the emergence of an industry around ID itself. Where a firm lies in pursuing the ID production functions, and the extent of human-computing interaction that is involved, reflects the extent of the firm’s evolution within the ID industry.

Future directions: Building on the definition of industrial data, the team is currently conducting interviews with large and small firms operating across the ID production process in various industries. Through this, we hope to learn more about the conditions under which the ID industry is evolving, and how countries and regions may develop competitive advantage within this space. The results of our study will be published in the near future, in collaboration with EPICenter.


[1] Boria, S., 2015. Developing Smart Tools for the Airbus Factory of the Future, INDUSTRIAL INTERNET IN ACTION CASE STUDY. Industrial Internet Consortium.

Plataine, 2017. Case Study: Harbin Hafei Airbus. Plataine, Waltham, MA.

GE Aviation, 2016. How GE’s Predix Is Taking Aviation Productivity to New Levels | GE Aviation.

[2] Davies, R., 2015. Industry 4.0. Digitalisation for productivity and growth. European Parliament Briefing 10.

[3] Evans, P.C., Annunziata, M., 2012. Industrial Internet : Pushing the Boundaries of Minds and Machines.

Accenture, GE, 2015. Industrial Internet Insights Report. Industrial Insights Report 1–35.

[4] Muro, M., Rothwell, J., Andes, S., Fikri, K., Kulkarni, S., 2015. America’s Advanced Industries: What They Are, Where They Are, and Why They Matter, Brookings Advanced Industries Project. Brookings Institution.

[5] Subcommittee of Advanced Manufacturing, 2016. ADVANCED MANUFACTURING: A Snapshot of Priority Technology Areas Across the Federal Government. The White House, Washington D.C.

[6] McKewen, E., 2015. CMTC Manufacturing Blog. What is Smart Manufacturing? (Part 1A).

[7] Courtney, B., 2014. Industrial big data analytics: The present and future. InTech 61.

[8] TechNet, SusSpec Alliance, DBL Partners, 2016. Unlocking Grid Data (White Paper).

[9] Manyika, J., Chui, M., Bisson, P., Woetzel, J., Dobbs, R., Bughin, J., Aharon, D., 2015. The Internet of Things: Mapping the value beyond the hype. McKinsey Glob. Inst. 144. doi:10.1007/978-3-319-05029-4_7.


Designing the Smart City: A Programmatic Approach to Inclusive Innovation in Atlanta [External]


This week, CUI Director Dr. Jennifer Clark had a post published in the Atlanta Studies blog. Atlanta Studies is “is an open access, digital publication of the Atlanta Studies Network,” which includes students, instructors, and researchers from Emory University, Georgia State University, Georgia Institute of Technology, Clark Atlanta University, Kennesaw State University, the Atlanta History Center, and the New Georgia Encyclopedia. Dr. Clark’s post, “Designing the Smart City: A Programmatic Approach to Inclusive Innovation in Atlanta,” discusses the emerging range of smart city interventions, and uses several examples from Atlanta, including the MetroLab Network and North Avenue Smart Corridor, and its role in the smart cities story to explain these important larger developments.

Smart cities are about sustainable economic development in the future – not just autonomous vehicles like the one that took a test drive on the North Avenue Corridor on September 14 – and that requires a programmatic approach to technological change, not just one, discrete technology project. In my forthcoming book on smart cities, Making Smart Cities: Innovation and the Production of New Urban Knowledge (Columbia University Press) I analyze smart cities from several vantage points. First, I discuss smart cities as a set of technocratic solutions to urban policy challenges – projects, programs, and products. Second, I describe how smart cities operate as emerging markets for new technologies. Third, I describe how smart cities are developing as a new form of urban entrepreneurship focused on marketing cities in a competitive global economy. Fourth, I explain how smart cities act as a mechanism for exacerbating uneven development. Fifth, I illustrate how smart cities are developed through distributed networks for innovative governance. And finally, I analyze the potential of smart cities as a means for increased civic engagement and open innovation. I argue that technology development is the easy part; it is the design for and deployment into this increasingly liminal space – twenty-first century US cities – where governance, regulation, access, participation, and representation are all complex and highly localized, that is the real challenge.
Atlanta is a key example of this challenge and underscores the importance of partnerships in the design and deployment of smart cities programs and policies.

To read the article in full, please visit its Atlanta Studies page.

To learn more about the North Avenue Smart Corridor, see the video below.

Wrangling Legacy Data: Preparing for Sociotechnical Change in the Smart City

by Thomas Lodato and Jennifer Clark

Center for Urban Innovation

From connected traffic signals and self-reporting trashcans to automated mobility vans and apps for reporting potholes, smart cities promise to make urban areas more efficient, increase the capacity and options of government and public services, and drive decision-making. These visions are predicated on the use of various advanced (and often computational) technologies that can reveal insights, inform decision-makers and citizens, predict outcomes, and automate processes. Undergirding these technologies—and their insights and efficiencies—are means to produce, circulate, and use data. These data are explicitly captured by sensors and devices, as well as are produced a byproduct or as “exhaust” of various systems. Through analysis and application, data fuel smart cities by attuning systems and people to macro- and micro-processes previously too difficult or invisible to act upon.

Given the contested state of smart city data, we embarked on a project to understand what challenges and barriers exist to making legacy data machine-processable in the smart city. Rather than account for technical barriers in isolation, we engaged in constructive design research (a practice-based methodology) to understand the cascade of dependencies embedded within data wrangling. Focused on budget data in the City of Atlanta—a prime example of legacy data ripe for transformation into machine-processable OGD (open government data)—we wrangled these data into structured data files. In particular, we created Google spreadsheets of budget data from 1996 to 2017 able to be exported to various structured formats (.XSLX, .CSV, .TSV). Motivating the production of these data files was the comparison of budgeted revenues and expenses to audited (“actual”) revenues and expenses.

This project was motivated by the following research question: what assumptions, barriers, and challenges exist within the sociotechnical practice of wrangling legacy data in the smart city? Put differently: what does data wrangling mean in the context of the smart city beyond the technical extraction, manipulation, and transformation of data? Through this practiced-based methodology, we learned that machine-processable OGD depend on an array of coordinated features in the smart city landscape, from where data is hosted and how it is documented to the embedded values of opening data and the tacit domain knowledge of data production.

In this blogpost, we give an overview of the insights from the legacy data project. A more detailed analysis will be available in a forthcoming whitepaper on the project.

The basic question driving our research was, with data so vital to the smart city project, why are data so scarce?

Where there seems to be a glut of proprietary, closed data—that is, data that some entity has exclusive control over (e.g. data that can be sold, and access and use can be restricted; see Tauberer 2014 for more)—other types of data more rare. In particular, the data that seems to be lacking are machine-processable open government data. Open government data—or OGD—refers to “non-privacy-restricted and non-confidential data which is [sic] produced with public money and made available without any restrictions on its use or distribution.” As such, OGD are data that are produced by and about governments, citizens, and businesses. Machine-processable data are data “reasonably structured to allow automated processing.” The Sunlight Foundation’s Open Data Policy Guidelines explain that machine-processable data are “[o]ne step beyond machine-readable data” by existing in “a format intended to ease machine searching and sorting processes.” As such, machine-processable OGD are one of type of data that are vital to understanding public processes, transactions, and affairs. Without these data, a host of potential insights and avenues promised by the smart city are non-starters.

One major challenge for the machine processability of OGD is the speed and character of technological change. Simply put, machine processability is not a static state. As anyone who has ever moved between two different computers knows, the ability to use files of varying formats depends on an entire system. Governments are slow to overhaul information technology (IT) systems and smart city technologies (platforms, standards, systems) seemingly change from day-to-day. The disparity in the pace of change means that governments adhere to a configuration of aging, obsolete, or outdated protocols, procedures, and infrastructures of data production, circulation, and use. We define this configuration as a legacy system. As such, these legacy systems produce data—namely, legacy data—that fail to be easily integrated into new smart city systems, and therefore are not machine-processable, even if they are open-access or publicly available.

In an effort to catalog the state of OGD in the United States, the Sunlight Foundation launched the US Open Data Census in 2014 in partnership Code for America and Open Knowledge International. Through hackathon-like events as well as ongoing assessment, the US Open Data Census provides resources to evaluate the availability of city-level OGD based on nine criteria: whether data exists, is openly licensed, freely available, machine-readable, available in bulk, regularly updated, publicly available, online, and in a digital form. The results highlight the scale of the challenge facing machine-processable OGD. Of the 1111 currently identified datasets across 101 US cities, only 627 (56%) are machine-readable. Similarly, only 601 (54%) datasets are downloadable in bulk. Even fewer—only 552 (50%)—are both machine-readable and downloadable in bulk. Though not equivalent to machine processability, the US Open Data Census reports that only 37% of public datasets in the US are, in fact, open. Ultimately, the machine processability of these data depends on the fulfillment of most, if not all, of these criteria. Even more, the percentage of total datasets that are actually machine-processable is likely to be even lower than the US Open Data Census might indicate because these percentages are based on cities that have local volunteers willing to sort through the available public data. As such, machine-processable OGD are far less common than one might assume.

One way to make OGD machine-processable is to migrate a legacy system to a newer system. Yet where new solutions that expand a city’s capacity are met with enthusiastic support, upgrading existing systems often are not. The cost of upgrading IT tends to be difficult to justify. Existing legacy systems often still work for their given purpose, even if these systems are on the verge of obsolescence. Even more, migrating a legacy system remedies only a part of the problem. The other part is transforming the data themselves to be integrated into an entirely new set of protocols, procedures, and infrastructures. This process is referred to as data wrangling (alternately called data munging), or the process of making data useful for a given end. In the smart city, usefulness means machine processability. Therefore, the goal is to transform OGD into appropriate structured formats. Although these two processes—migrating systems and wrangling data—are related, they are distinct, each of which presents challenges.

But legacy data are more than old files. Changing IT requires retraining personnel, restructuring administrative procedures, and reformatting what data are collected, stored, and accessed. In this way, governments (and many organizations) are slow—technological change implies changes in long-established and deeply entrenched administrative practices, usage expectations, and hosts of other social factors. As such, the challenge of legacy data for smart cities precedes computerized systems and extend beyond the immediate reach of such systems. In short, making legacy data into machine-processable OGD require more than a technical fix.

In this blogpost, we give an overview of the insights from the legacy data project. A more detailed analysis will be available in a above-mentioned forthcoming whitepaper on the project.

Insight 1: Domain Knowledge is Important to Data Wrangling

Although budgets are orderly documents, as a dataset, they are complicated. In part, this complexity comes from subtle yet important distinctions about what a budget actually is. In order to explain what we mean we need to explain how budget documents are made.


Figure 1: Diagram of budget process

The City of Atlanta budget process follows the production of three primary documents. The first document is the proposed budget, which is created by May in preparation for the upcoming fiscal year (July to June). This document is created by the Office of the Mayor, and takes into consideration the priorities submitted by Atlanta City Council’s Finance/Executive Committee in a nonbinding resolution. The second document is the adopted budget. The adopted budget is created out of the debates and negotiations of full City Council with regards to the proposed budget. By the end of June, the City Council must adopt the budget. The third document is the comprehensive annual financial report (CAFR). This document is created at the close of the fiscal year and reports the audited expenses and revenues of Atlanta’s city government. The CAFR is then used to help set priorities in the subsequent fiscal year. (See Figure 1 for a diagram of the process.)

In many ways, the process of producing these three documents—the proposed budget, the adopted budget, and the CAFR—are split. The first two documents are documents that look forward; the latter document looks backward. As such, the proposed and adopted budgets are truly budgeting documents in that they estimate expenses and revenues. In other words, budgeting is a type of planning. The CAFR, on the other hand, is an auditing document in that it is an inspection of the accounts and activities. Between planning and auditing, commitments are implemented as revenue is generated and expenses are deducted over the course of a fiscal year (and all that can happen in any given year). Although the CAFR provides data on the actual expenses and revenue, these data are not, say, higher fidelity than proposed or adopted budget data, but instead refer to different processes altogether. That is to say, the data enclosed in these different documents have continuity, but are not the same. The proposed and adopted budget data are data about projections, and so constitute promises made by civil servants and elected representatives to the public (the Mayor and City Council in the proposed and adopted budgets, respectively). The CAFR, on the other hand, is an account of whether and to what degree these projections and promises were met. Without marking this distinction, one could confuse value of these different documents and the usefulness of the different data.

In order to make budget and audit data useful, one must first understand what types of evidence these data might produce. Where machine processability is the default technical goal of wrangling OGD within the smart city, machine processability must be coupled with data that are meaningful and understandable to those seeking new insights. Before engaging in any process of wrangling, what is being wrangled must be understood. In the case of budget data, the production of these three documents impacts what types of insights one might gain. This is but one instance where domain knowledge became important to the data wrangling process as we will see in the next section. Other instances—such as the shift from a January-to-December fiscal year to a July-to-June in 2007 (see below)—impacted what and how we collected, extracted, normalized, and documented the data.

Insight 2: Wrangling begins with collection

In order to transform data into a machine-processable format, you must first have data. As such, our initial step in wrangling data was collecting files to establish the data corpus. The data corpus constitutes the known extent of the data relevant to a particular topic (here, Atlanta’s revenues and expenses). But collection is ultimately driven by what we are interested in understanding through data. In this way, collection is always oriented toward a question answerable with data. Motivating our project was a comparative question: how well do budgeted revenues and expenses compare with audited (actual) revenues and expenses? From this question, the data corpus becomes actionable.


Figure 2: Sources of budget data

Managed by the Department of Finance, expense and revenue data are released through two channels (see figure 2). The first channel is the Department of Finance webpages located on the City of Atlanta website. On these webpages, budget data are available in portable document formats (PDFs). The data are released in different discrete documents that must be downloaded individually. Proposed budgets and adopted budgets are released on the Budget and Fiscal Policy subpage. Currently, these documents can be downloaded dating from 2010-2018. Also available on this subpage are monthly financial reports from fiscal year 2010 to December of fiscal year 2017, and five-year financial planning documents from fiscal year 2011 to 2018. Adopted budgets (“budget books”) dating back to 1996 can be downloaded on the Historical Budget Documents sub-subpage. Auditing documents are found on the Controller subpage. This subpage contains both digest documents of the City’s performance (Popular Annual Financial Reports or PAFRs) from 2012 to 2016, and the more detailed CAFR documents from 2002 to 2016.

The second channel for expense and revenue data is the Atlanta Budget Explorer (ABE) website, a visualization of expense and revenue data hosted by the City of Atlanta and built with Tableau. Conceived during the 2nd annual Govathon in 2013, the ABE is designed to show Atlantans how and where Atlanta city government spends and generates money. The ABE provides information on four of the City’s major funds: General Fund, Trust and Pension, Enterprise Fund, and Service Revenue Fund. The underlying data on the site are primarily derived from the CAFR. Currently “actual” revenue and expenses are available for 2012 to 2016. Expected revenue and expenses—i.e. budget data—are derived from the adopted budgets for 2017 and 2018. A collated dataset is downloadable from within the ABE as a Tableau worksheet, PDF, Excel file (XLS), CSV, or image (PNG of a visualization). The available data files are individually downloadable based on the particular visualization of funds and departments, and are not able to be bulk downloaded on this or any other site.

As already mentioned, confusing budget data and auditing data presents a problem from the perspective of the kinds of questions one can answer with data. In particular, the ABE presents data that seems to answer the question “Where has money actually gone/come from?” The question that motivates our research is “How well does the City of Atlanta budget?” This second question requires the comparison of budget data and auditing data.

As for the process of wrangling, collection reveals the extent of the task and where to focus efforts. With the data from the ABE being primarily auditing data, we realized the data corpus greatly exceeded the existing machine-processable OGD found within the ABE. Instead, the various PDF documents adopted and proposed budgets housed the data we were after. Given the absence of these data in machine-processable formats, the PDF files became the primary subset of the data corpus and clearly defined our next steps in extracting, schematizing, and documenting the data. Additionally, with the question of comparing budgeted values for expenses and revenues with their audited counterparts, we focused on the adopted budgets rather than proposed budgets because adopted budgets represent the combined priorities of both the Mayor and City Council. (Again, domain knowledge matters!)

To reiterate, data wrangling means making data useful, and being useful is dependent on the context of use. Where collection seems exterior to the manipulation and transformation processes defined by data wrangling, collection is vital to establishing the context in which a particular question is answerable through data.

Insight 3: Extraction requires synthesis (and/or why automation may not help)

A primary task within legacy data wrangling is extracting data. Extraction entails pulling data from one file into another file for the purposes of further cleaning, ordering, and (re)formatting. In the context of legacy data generally—and specifically with our project—extraction can be a time-consuming, manual process. In terms of time spent, extraction dominated our project work. Where automation may help, it can also compromise data quality and obfuscate telling idiosyncrasies within the data.

Synthesis, on the other hand, is the process of creating data, either through calculations or other manipulations performed on data. In many ways, extraction and synthesis seem to be opposing processes. Extraction being rote translation from one file to another and synthesis being active creation and manipulation. Yet, as we found, extraction and synthesis are sometimes simultaneous in order to produce a data file that are meaningful and complete.

Targeting only adopted budgets, we began to comb through these documents dating from 1996 to 2017. The first challenge was that the quality of the PDFs changes dramatically across the corpus. Newer PDFs were created digitally and so were already searchable. This feature allowed us to easily locate specific funds and line items. Older PDFs, however, were scanned paper documents, and therefore not immediately searchable. Rather than look through these documents completely manually—meaning, visually line-by-line—we performed optical text recognition (OCR). Due to the font and visual artifacts produced from the original scan, OCR was only partially successful. As such, searching older adopted budgets requires us to perform a second manual pass to confirm no data were missed.


Figure 3: An error in the 2003 adopted budget

The next challenge was that adopted budget documents are created by humans and therefore contain errors. For example, in the 2003 adopted budget, two different values were used for the total value of the 2002 Proprietary Funds—$2,768,172,365 on page 35 and $3,740,664,687 on page 112 (see figure 3). Upon checking the value against other documents, the latter value appears to be a typo. The question is how should we account for this discrepancy in our data file? For this particular cell we recorded the verified value ($2,768,172,365 from page 35) but also produced cell-level metadata that cites the page number of the source and notes the error in the original document. This strategy was extended to all cells in our data set to account for our own potential for producing human error through data entry and allow for others to inspect our process. In this way, the extracted data are accompanied with a map of how the data was extracted in the form of transformational metadata in order to inform users of the data about why a specific value is listed. Here extraction itself synthesized (meta)data.

A third challenge for extraction—and one that also impacted and stemmed from schematization and normalization (see the next section)—was how funds changed over time. As such, certain summary values of particular funds were not always listed in the document. In some instances, these values needed to be calculated, such as the change (in percentage) from one year to the next of a particular fund. In other instances, funds that did not exist or no longer existed required a designation that distinguished amongst non-numeric entries. We created a system to distinguish amongst funds that had no mention in a particular document (marked with “NM”), values that were pending (e.g. audited values for future years [marked with “FY”], or documents yet-to-be-reviewed [marked with “TBD”]), and values required calculation (e.g. summations of funds; marked with “CALC”). Here again, extraction requires synthesis as this classification scheme distinguished cells with a zero (i.e. a listed value of zero) from empty values.

Reflecting on these challenges reveals that automating extraction, although certainly time saving, may jeopardize data quality both in terms of the veracity and the verifiability of the data. In the first case, automation does not account for errors in the data; in the second case, automation does not track the origins of extracted data. In both cases, poor data quality may undermine claims made with data, and compromise the usefulness of OGD.

Insight 4: Schematization and Normalization Are Iterative

Schematization refers to the creation of an organizational structure for data, both in terms of architecture (how data are ordered and arranged as a set) and entry (how an individual datum is recorded). Normalization refers to the standardization of data in light of inevitable variations across different schemata. These two processes create data such that they can be processed in systematized ways, whether that means being joined with other data sets, algorithmically analyzed, or some other machine process. We have already touched on normalization in the previous section with regards to distinguishing between types of empty cells.

Although these processes are central to all data, they are especially important to legacy data wrangling. Legacy data are defined by a change in the protocols, procedures, and infrastructures of data production, circulation, and use. These changes often—if not always—entail changes to the architecture of a dataset as well as the conventions of data entry and collection. Even more, given that legacy data may extend across a large timescale, the potential for multiple explicit and/or implicit changes to organizational and entry standards are possible. In the budget data corpus, this final point was certainly true.


Figure 4: Budget data file architecture

After collecting the corpus and deciding to focus on adopted budgets, we initially extracted data from sample of years (1996, 1998, 2003, 2008, 2012, 2017) to understand the organization of these files and discern an architecture. From these years, we determined an overarching organization, of which we adopted a three-tiered structure (see figure 4 for details). The first tier are fund groups, or groupings of revenue and expenses based on how the money is procured and can be used. These fund groups are Governmental Funds (funds supported by taxes, grants, special revenue, and other sources), Proprietary Funds (funds related to operations and revenues from governmental services), and Fiduciary Funds (funds held in trusts or unable to support governmental activities, e.g. pensions). The second tier are funds, or allotments of money allocated to particular functions, such as the General Fund (money for departmental operations) or Special Revenue Fund (money for specific projects). The third tier are subfunds, or alloments of money for specific purposes, such as the Intergovernmental Grants Fund within the Special Revenue Fund.

Although this high-level architecture carries across the entire data corpus, variation within this framework required iteration on the specific elements. For example, between fiscal year 2013 and 2014, the Group Insurance Fund switched from Fiduciary Funds to Proprietary Funds, respectively. Where the Group Insurance Fund persisted across years in name, its funding sources changed and therefore it exists in two different fund groups. Even more, the shift changed the Group Insurance Fund from a subfund (Proprietary Funds > Internal Service Fund > Group Insurance (Sub)Fund) to a fund (Fiduciary Funds > Group Insurance Fund). In synthesizing data on the percent change between 2013 and 2014, we needed to annotate the data point with cell-level metadata. The annotation noted the change in where the fund was located as a caveat to the percentage change in the fund. At the organizational level, we decided to duplicate the fund name, thereby rendering the name of a given fund group, fund, or subfund no longer unique. This resulted in adding an additional unique identifier in a separate column. The unique identifier was necessary for machine processability as a data structure is most useful when it is well defined.

Another issue with regards to schematization arose from a shift in the timeframe represented by the data. In 2007, the fiscal year changed and was recorded in a one-page document found instead of a full budget for 2007. Budgets preceding 2006, adhere to a calendar fiscal year, spanning January to December. Budgets after 2007, adhere to a July-to-June fiscal year. As such, comparing budgets from 1996 to 2016 compares different timeframes, though still comparing fiscal years. In organizing data by year (columns correspond to documents from a given fiscal year), the current architecture obfuscates the change in what a fiscal year signifies. In this way, listing values by year make 1996 and 2016 comparable despite changes to their actual timeframe, thereby normalizing the data through schematization.

These different instances illustrate that schematization and normalization are an ongoing and iterative process. As data are added to a dataset, the organizational architecture, entry-level schemata, and processes of standardizing are tested. These new data reveal where the structures and standards are consistent, but also where modifications need to be made. Rather than indicating that the initial schemes and norms are incorrect, these iterative adjustments reveal that any schemes and norms depend on the scope and scale of the data. In order to make data machine-processable, one must adjust these structural features and standards to adhere to the specific demands of the machine process. Yet, adjustments require adequate documentation to reveal potentially obfuscated assumptions.

Insight 5: Documentation is not just about data but about process

Where data themselves can provide meaningful insights into phenomena, these insights depend on the quality of the data. Data quality stems to the granularity, collection method, frequency, and timeliness of the data in answering a particular research question. Some of these features of the data can be directly assessed from the dataset (e.g. granularity), but others—such as collection method, and when and by whom the data was produced—are only knowable through metadata. With a data corpus spanning many legacy systems, documentation standards often vary, leading to issues with verifying data quality.

Most often metadata provide valuable information about who, where, when, how, and occasionally why data are produced. According to Kitchin (2014), metadata fall into three categories: descriptive metadata, or data about what the dataset is called, what it includes, who produced it; structural metadata, or data about the organization and scope of the dataset; and, administrative metadata, or data about how the dataset was produced, including file format, ownership, and license. In our project we created metadata that describe the structure, collection methods, and contents of the dataset. In these files, we explain the ways the data changed overtime (e.g. the fiscal year shift), the norms and schemes for ordering the data (e.g. how unique identifiers work and the descriptions of the tiered structure), and where the data came from (e.g. what files are sources and where those files came from). Even with all of these metadata, still some aspects of the data production were missing.

Extracting and synthesizing required us to account for the particular page(s) where a datum was found. These annotations allowed us to document where we found errors or typos. Additionally, tracking where a datum came from offered a means for us to mitigate the introduction of our own errors through a transparent process. These cell-level (or entry-level) annotations constitute a fourth category of metadata—transformational metadata. These metadata track the actions taken to create a particular value or file. At this very granular level, metadata aid data quality by revealing the original source material (page numbers of where a value comes from), including errors, typos, and annotations about different synthesized values. These metadata offer insight into how the dataset was produced; that is, transformational metadata are data about wrangling itself.

In the case of legacy data, transformational metadata are vital. With a host of potential variations, artifacts, and even errors from different legacy systems, documenting how legacy data were wrangled allows data analysts and researchers to inspect data production. By doing so, the process becomes data that can be analyzed and verified.


Our project sought to answer what assumptions, barriers, and challenges exist within the sociotechnical practice of wrangling legacy data in the smart city? These five insights provide a series of conclusions about the pressing challenges for smart cities.

  1. Questions Drive Data: Although many claim open data hold nearly limitless insights, the project highlights that these insights are only as good as the questions being asked of data. Without a clear understanding of how data are useful for a given end, efforts to open data are more than likely to be aimless, reinforcing foregone conclusions rather than producing new insights. Even more, collecting data because they exist undermines the relationship between data and conclusions by confusing exploratory research with descriptive research. As such, being explicit about the questions driving the production of machine-processable OGD attunes conclusions and fosters different questions, thereby motivating data release.

  2. Internal Capacity/Knowledge Cannot Be Overlooked: With increased emphasis on public-private partnerships, or city-university partnerships, or even subcontracting, smart cities projects are often accomplished through the extension of local government capacity through a contractual relationship. As such, local governments may be missing out on driving the agenda of smart cities by not developing internal capacity. Where companies can move fast, governments move slow, and slowness can be an asset when it comes to institutional memory related to the particular governmental context. For smart cities to thrive, change—technological or otherwise—needs to be paired with a long-term strategy. Local governments can to be the bearers of that strategy and can only do so effectively by building internal capacity and knowledge to establish appropriate resources (or leverage existing resources), develop new programs and projects, and negotiate contracts that align internal best practices.

  3. Prepare for Change with Interoperability: The value of smart cities is derived from the comprehensive and integrated array of technologies and processes. With so much flux, taking a long-view on change is important. This long-view prepares for change by assuming no project, system, or dataset exists in isolation. Where companies sometimes (maybe often) push proprietary systems, local governments can push back and think about data ownership and governance in the terms of a different timescale. Here again, internal capacity and knowledge are vital. OGD are only a portion of the data landscape of a smart city. Open data, more generally, decouples data from systems. Although not always possible, establishing open data standards for all systems makes migration from one system to another easier by establishing an expectation of interoperability. Additionally, establishing open data standards also establishes administrative practices for wrangling data by creating expectations that data and systems require different attention and skills.
  4. The Smart City is the Documented City: If data are the fuel of the smart city, then metadata are the pipeline and logistics network. To foster insights from data, local governments need to set standards for not just the release of data, but the adequate documentation of data. Documentation allows smart cities to learn from their past rather than just react to the present.

Dr. Clark Speaks at the Regional Studies Association: The Evolutionary Economic Geography and the Policy Nexus

GT CUI’s Director Jennifer Clark will be speaking at the Regional Studies Association’s Annual Conference in Dublin, Ireland on June 6th. Dr. Clark, who is from the School of Public Policy at the Georgia Institute of Technology, will join her colleagues Bjørn T. Asheim, University of Stavanger, Norway and Lund University, Sweden; Ron Boschma, Utrecht University, the Netherlands; Martin Henning, University of Gothenburg, Sweden, Andy Pike, Newcastle University, UK; Mark Deakin, Edinburgh Napier University, UK; and Nicos Komninos, Aristotle University of Thessaloniki to discuss Smart Specialisation and Evolutionary Economic Geography: Essential Symbiosis in Order to Advance the Agenda? The panel will be held on Tuesday 06/06/2017, 11:30-13:00 in Emmet Theatre (TCD Conference Centre – Arts Building) at Trinity College in Dublin.

The panel was organized by Dieter F. Kogler, University College Dublin, Ireland; Luca Mora, Politecnico di Milano, Italy; and Mark Deakin, Edinburgh Napier University, UK and will be moderated by Dieter Kogler.

The session is part of the RSA Conference’s Discuss and Debates series and will explore how recent progress in the field of Evolutionary Economic Geography can support the ambiguous European project of “Smart Specialisation”.  Here the focus is directed at science and technology domains and in particular at their presence and connectedness at a given place.  However, much of the evidence supporting Smart Specialisation theories is anecdotal.

Evolutionary Economic Geography on the other hand is working on a number of systematic approaches capable of identifying the local knowledge bases, while also measuring how relatedness among such domains advances over time in a path-dependent fashion.

Based on this the following idea has been put forward: if one manages to quantify domain and connectedness, ceteris paribus, one should also be able to predict future trajectories of regional development, and thus be able to advise regions in what areas of economic activity to invest on order to create a competitive edge that rests on local scientific and technological expertise that is difficult to replicate elsewhere.

Panelists will discuss the feasibility of such an idea in light of recent theoretical and empirical advancements.

Dr. Clark will also speak at a pre-conference workshop on Evolutionary Economic Geography at University College Dublin on Sunday, June 4th on how Evolutionary Economic Geography has (or has not) influenced science and innovation policy and future prospects for building buildings between the academic work of EEG and the policy design and implementation schemes defining investment in science and innovation.


Financing Affordable Housing with the LIHTC [External]

by CUI Staff


Recently, CUI’s Graduate Research Assistant Chris Thayer was published in the Federal Reserve Bank of Atlanta’s Partners Update feature. The article, “Financing Affordable Housing with the LIHTC,” covers the basics of the LIHTC program, the operation of LIHTC’s sometimes underutilized 4% Credit, and discusses the benefits and challenges of working with this less common credit type.

The availability of affordable housing in the Southeast is an increasing challenge, especially in areas of opportunity that have jobs, good schools, public transit, and so forth. The Atlanta Fed’s Community and Economic Development discussion paper “Declines in Low-Cost Rented Housing Units in Eight Large Southeastern Cities” puts some numbers to the depth of the problem, with metro Nashville showing a loss of over 7,700 low-cost rental units between 2010 and 2014.

In light of these challenges, sources of funding for affordable housing are more important, and competitive, than ever. One of the best-known and most competitive sources of funding for affordable housing construction is the Low-Income Housing Tax Credit program, or LIHTC. The most common form of the program, the 9 percent credit, is already a highly sought-after affordable housing construction subsidy, but LIHTC also offers a noncompetitive 4 percent tax credit. This 4 percent credit, typically paired with the development of affordable housing financed with state or local government-issued tax-exempt bonds, is a less often utilized financing strategy and still an important tool in the affordable housing tool kit (Novogradac, 2016e).

To read the article in full, please visit its Federal Reserve page.

Smart Japan: Observations from an interdisciplinary urban design studio

by Emma French

Shibuya Crossing in Central Tokyo, reportedly the busiest pedestrian crossing in the world

In March, I had the incredible opportunity to travel to Japan for a week with 18 students for Georgia Tech’s Smart City Urban Design Studio led by Professor Perry Yang. The purpose of the studio is to explore how smart city technologies and tools such as 3D GIS, urban energy modeling, eco district certifications such as LEED ND, IoT (Internet of Things), pervasive computing, and big data can be incorporated in design processes to support the shaping of ecologically responsive, resilient, and human sensing urban environments. Comprised of Georgia Tech graduate students in city planning, architecture, policy, industrial design, and interactive computing, the studio represents a collaboration between Georgia Tech, the Global Carbon Project (GCP), the National Institute for Environmental Studies (NIES), and the Department of Urban Engineering of the University of Tokyo.

At the beginning of the studio, we divided ourselves into four groups based on our interests and areas of expertise: Conceptual Design (mostly made up of architects), Performance Modeling (mostly planners), Smart City Computing (a mix of industrial designers, interactive programmers, and planners), and Community Engagement (planners and policy students).

Our task: To design a framework for the smart development of a satellite city called Urawa Misono in Saitama Prefecture, Japan. About 45 minutes from Tokyo by train, Urawa Misono is the last stop on the the Saitama Rapid Railway Line. Every two weeks thousands of REDs soccer fans swarm the station and walk or drive to the massive Saitama Stadium that was constructed in 2002 to host the FIFA championships.

Georgia Tech students walking from the train station to Saitama Stadium on their site visit in Urawa Misono

Saitama Stadium will be an important site for the 2020 Olympics, prompting local and regional officials to think about how they are going to accommodate the massive influx of people coming in for the games. Even without the Olympics, Urawa Misono’s current population of a little over 7,000 is expected to triple in size to over 32,000 by 2030. To top it all off, the national government has identified Urawa Misono as a potential site of smart development, leading to increased investment in the area by smart city leaders, such as Toyota and IBM.

Japan is already considered one of the smartest countries in the world, with its tech savvy population and concentration of tech conglomerates. Japan’s national Smart ICT Strategy published in 2014 by the Ministry of Internal Affairs and Communications laid out the country’s goal of becoming a global leader in ICT innovation by 2020.

We experienced many of Japan’s smart technologies in our first hours on the ground. From the public toilets that have heated seats and play music to ensure privacy, to the heated floors in our residence, the most obvious innovations seemed closely tied to individual human comfort. Other innovations, such as the rapid transit systems and compact residential developments focused more on efficiency and convenience than individual comfort.


Top: Smart toilets in Japan allow you to adjust the temperature of the seat, play music, and flush by simply waving your hand in front of a sensor. Bottom: Japan’s extensive urban rail network transports 40 million people daily. Biking is so prevalent on the University of Tokyo’s campus that individuals are required to register for a parking spot at $20/month.

Due to the purpose of our visit, I found myself noticing things that I probably would have overlooked on any other trip. Things like the reflectors set up along the highway that eliminated the need for energy intensive overhead street lights. Or the six different types of trash and recycling receptacles lined up in Ueno park. Perhaps the most intriguing innovation was a road in rural area that we visited that played a song as your drove over it. The purpose of the musical road was to announce to visitors that they were entering a particular region known for its fruit production.

Our studio forced me to think not just about the initial purpose of these smart innovations, but also about their ongoing performance. Leading up to our trip, one of the biggest challenges for us as a studio had been effectively integrating the work being done by each of the subgroups into one coherent proposal. During an initial charrette we came up with our own parameters for a smart city, as one that is sustainable, adaptable, and equitable. Designing a framework for the development of such a place—in a country we knew very little about—proved exceedingly difficult. As the conceptual design team drew initial plans and the performance modeling group came up with performance measurements with which to evaluate those plans, the smart city computing team grappled with the challenge of creating adaptable public spaces and structures and the community engagement team attempted to use technology to communicate with residents in Urawa Misono to ensure that our studio’s proposals were grounded in local customs and needs.

The challenges faced by our studio—making our design proposals sustainable, adaptable, and locally relevant—are some of the fundamental challenges facing smart city initiatives around the world. While smart infrastructure has the potential to improve urban functionality, in order to create truly smart cities we need to be continuously evaluating them based on more than just technology deployment. A comprehensive, ongoing evaluation system, perhaps something along the lines of Bloomberg’s newly released National Standard for Data-Driven Government, is needed to ensure that smart city initiatives are not solely about technology, but also about achieving long-term efficiency, addressing local needs, and promoting equity.

To learn more about the Misono Smart City Studio check out of blog: https://waterfrontcities.wordpress.com/

Dr. Jennifer Clark at the American Association of Geographers Annual Meeting in Boston


by Jennifer Clark and Thomas Lodato

170403 blog post
Courtesy University of Oxford

The annual meeting of the American Association of Geographers (AAG) convenes in Boston, Massachusetts from April 5th-9th, 2017. The event features nearly 7000 presentations, posters, workshops, and field trips by leading scholars, experts, and researchers. Founded in 1904, the AAG is a nonprofit, scientific and educational society aimed at advancing the understanding, study and importance of geography and related fields. The AAG currently has more than 10,000 members from nearly 100 countries working in geography or related fields in the public, private, and academic sectors. Members are able to engage by attending the AAG Annual Meetings, publishing in the association’s scholarly journals (Annals of the American Association of GeographersThe Professional Geographer, the AAG Review of Books and GeoHumanities), and participating in the association’s affinity groups and more than 60 specialty groups and committees.

Georgia Tech Center for Urban Innovation Director and School of Public Policy Associate Professor, Dr. Jennifer Clark, will be in attendance presenting her work on the “contested market space” of smart cities in the paper Smart Cities: Remaking Markets and Manufacturing Open Innovation Spaces on Thursday, April 6th.

On April 7th Dr. Clark is speaking on urban and regional development past, present, and future on a panel celebrating the 40th anniversary of Newcastle University’s Centre for Urban and Regional Development Studies (CURDS).

As chair of Economic Geography Specialty Group (EGSG) and an editor of Regional Studies—the flagship journal of the Regional Studies Association (RSA)—Dr. Clark has co-organized a series sessions on economic geography, co-sponsored by EGSG and RSA. These sessions range from knowledge and firm networks in regional economies to innovation processes to the impact of policy and planning on urban economic activities.

Also on Friday, Dr. Clark will be chairing a panel on the life, work, and legacy of the late Dr. Susan Christopherson of Cornell University, her former adviser, co-author and frequent collaborator. The panel will include Professor Meric Gertler, President of the University of Toronto; Professor Amy Glasmeier of MIT; Professor Jane Pollard of Newcastle University; Professor Michael Storper of UCLA, the LSE, and ‘Sciences Po’ [Institut des Sciences Politiques]; Shanti Gamper-Rabindran of the University of Pittsburgh; and Professor Katharine Rankin of the University of Toronto.

On Tuesday, Dr. Clark will attend a pre-AAG workshop on ‘Clusters and related diversification’ at the Center for International Development at Harvard Kennedy School. The workshop will focus on the role of inter-industry relatedness, clusters, and product spaces in regional economic development.

In February of 2017, Dr. Clark was elected to the American Association of Geographer’s (AAG) Nominating Committee, one of only two nationally elected committees within AAG. Members of the Nominating Committee are uniquely responsible for nominating the AAG President, Vice President and National Councilors who determine the organization’s priorities and strategic direction.

Below is a rundown of the various activities Dr. Clark will be participating in as a presenter or chair. More information on the economic geography sessions by searching “economic geography” the online program or conference mobile app. (An asterisk [*] denotes the presenting author.)

Economic Geography VII – Technological Diffusion and the Economic Geography of New Production Spaces
Thursday, 4/6/2017, from 10:00 AM – 11:40 AM in Back Bay Ballroom A, Sheraton, Second Floor

Dieter Franz Kogler – University College Dublin
Pierre-Alexandre Balland – Utrecht University
Jennifer Clark – Georgia Institute of Technology 

Dieter Franz Kogler – University College Dublin 


10:00 AM
*Laura Wolf-Powers – Center for Urban Research, City University of New York
Marc Doussard – University of Illinois Urbana-Champaign
Greg Schrock – Portland State University
Makers and the New Manufacturing Policy

10:20 AM
*Marc Doussard – University of Illinois at Urbana-Champaign
Laura Wolf-Powers, PhD – CUNY Graduate Center
Greg Schrcok, PhD – Portland State University
Max Eisenburger – University of Illinois at Urbana-Champaign
The Maker’s World of Production: Scaling up from Tinkering to Manufacturing in Three U.S. Cities

10:40 AM
*Mariachiara Barzotto – University of Birmingham
Occupational Mix for Sustainable and Inclusive Regions

11:00 AM
*Jennifer Clark – Georgia Institute of Technology
Smart Cities: Remaking Markets and Manufacturing Open Innovation Spaces

CURDS 40th Anniversary – urban and regional development: retrospect and prospect
Thursday, 4/6/2017, from 8:00 AM – 9:40 AM in New Hampshire, Marriott, Fifth Floor

Jane S. Pollard – Newcastle University
Danny Mackinnon – Newcastle University
Andy Pike – Newcastle University 

Jane S. Pollard – Newcastle University 

Ron Boschma – Utrecht University
Jennifer Clark – Georgia Institute of Technology
Maryann Feldman
Danny Mackinnon – Newcastle University
Jamie Peck – University of British Columbia 

Session Description:
Urban and regional development theory and policy confronts tumultuous times in terms of economic shifts, social and spatial inequalities, environmental tensions and geo-political turbulence across the world. Recognising and celebrating the 40th anniversary of the Centre for Urban and Regional Development Studies (CURDS) at Newcastle University, this panel debate reflects upon the retrospect and considers the prospect of urban and regional development. Connecting with the central research themes of CURDS work on ‘people and places’, ‘innovation and technology’, ‘finance and services’ and ‘institutions and governance’ over four decades, the aim of the dialogue is to better understand/elucidate where urban and regional development studies have come from in conceptual, theoretical, empirical and policy terms and to outline where its future directions are/might be heading.

Celebrating Susan Christopherson: A Panel Honoring her Life, Work, and Leadership in Economic Geography
Friday, 4/7/2017, from 1:20 PM – 3:00 PM in Room 109, Hynes, Plaza Level

Organizer and Chair:
Jennifer Clark – Georgia Institute of Technology

Session Description:
Susan Christopherson, an economic geographer and professor of city and regional planning known for her scholarly work and expertise on regional economic development, died December 14, 2016 of cancer. This panel celebrates and honors Susan’s accomplishments and leadership in the field of economic geography. The panel includes discussions of Susan’s contributions by several colleagues and collaborators from throughout her career as well as an opportunity for attendees to share their own experiences with Susan and her work.

In 2016 Susan received the Sir Peter Hall Contribution to the Field Award from the Regional Studies Association. In making the award, Professor Ron Martin of Cambridge University noted, “Over the years Susan has been a leading beacon in regional development studies, contributing some of the landmark papers in the field, and exerting a formative influence on both the theory and practice of economic geography internationally.” In 2015, Christopherson received the American Association of Geographers Lifetime Achievement award.

Susan’s research and teaching focused on economic development, labor markets, and location patterns in new media and film, advanced manufacturing, and resource extraction industries. She coauthored Remaking Regional Economies: Power, Labor, and Firm Strategies in the Knowledge Economy, winner of the 2009 Regional Studies Association Best Book Award. She published more than 100 articles and policy reports over the course of her career and served as an editor and on the editorial boards of several leading journals (including Regional Studies) and was also editor of the Regional Studies Association’s Regions and Cities Book Series.

Susan Christopherson was born March 20, 1947 in St. Paul, Minnesota.  She earned her bachelor’s degree in urban studies and a master’s in geography from the University of Minnesota. Susan earned her doctorate from the University of California-Berkeley in 1983.

Susan joined the faculty at Cornell University in Ithaca, New York in 1987. She was appointed Chair of Cornell’s Department of City and Regional Planning in 2014. Susan was the first woman to be promoted to full professor in city and regional planning at Cornell, and the first woman to chair the department in its nearly 80-year history.

Economic Geography III – Planning, Policy, Institutions, and Economic Performance
Wednesday, 4/5/2017, from 12:40 PM – 2:20 PM in Back Bay Ballroom A, Sheraton, Second Floor

Dieter Franz Kogler – University College Dublin
Jennifer Clark – Georgia Institute of Technology
Peter Kedron – Oklahoma State University 

Jennifer Clark – Georgia Institute of Technology 


12:40 PM
*Jun Du – Aston University
Tomasz Mickiewicz, Professor – Aston University
Ying Zhou, Dr. – Birmingham University
Productivity, Entrepreneurs and Big Firms in Shenzhen and Shanghai: Two Cities, Two Tales

1:00 PM
*Helen Lawton Smith – Birkbeck University of London
Sharmistha Bagchi-Sen – Department of Geography, SUNY-Buffalo
Emergence and convergence in a Bio-region in the UK: the ‘Golden Research Triangle’ in London and the South East

1:20 PM
*Donald Anthony Planey – Geography, University Of Illinois, Urbana Champain – Urbana, IL
Chicagoland and the Planning Imperative

1:40 PM
*Patrick Kilfoil – McGill University
Measuring the Impact of Spatial Planning Policies on the Economic Performance of Metropolitan Regions in the European Union


Economic Geography IX – Novelty, Access, Diffusion and Networks in Regional and Sectoral Development
Thursday, 4/6/2017, from 3:20 PM – 5:00 PM in Back Bay Ballroom A, Sheraton, Second Floor

Dieter Franz Kogler – University College Dublin
Jennifer Clark – Georgia Institute of Technology
Thomas Kemeny – University of Southampton 

Jennifer Clark – Georgia Institute of Technology 


3:20 PM
*Peter Kedron – Oklahoma State University
Dieter Kogler – University College Dublin
The Spatial Diffusion of Innovation in Renewable Energy: The Effects of Proximity of Biofuel Patenting

3:40 PM
*Tamás Sebestyén – MTA-PTE Innovation and Economic Growth Research Group
Attila Varga – University of Pécs
Knowledge networks in regional development: An agent-based model and its application

4:00 PM
*Giuseppe Calignano – UiS Business School – University of Stavanger
Rune Dahl Fitjar – UiS Business School – University of Stavanger
Dieter Franz Kogler – School of Geography Planning Environmental Policy – University College Dublin
Firm networks and knowledge production in a southern Italian aerospace district

Neil Lee – London School of Economics and Political Science
*Davide Luca – Harvard University
The big-city bias in credit markets: Evidence from firm perception’s in 100 countries

4:40 PM
*Teresa Farinha Fernandes – Utrecht University
Miguel Amaral – IN+ Center for Innovation, Technology and Policy Research, Lisboa University
Nuno Ferreira – IN+ Center for Innovation, Technology and Policy Research, Lisboa University
Pierre-Alexandre Balland – Utrecht University
Andrea Morrison – Utrecht University
Jobs relatedness and employment structure renewal in the Aeronautics

Economic Geography V – Intersections, Relations, Routines, and Collaborations in Innovation Processes
Wednesday, 4/5/2017, from 4:40 PM – 6:20 PM in Back Bay Ballroom A, Sheraton, Second Floor

Dieter Franz Kogler – University College Dublin
Peter Kedron – Oklahoma State University
Pierre-Alexandre Balland – Utrecht University 

Jennifer Clark – Georgia Institute of Technology 


4:40 PM
*Richard Shearmur – McGill University
David Doloreux – HEC Montréal
The nature of interactions between KIBS innovators and KIBS providers during the innovation process: does it alter depending on geographic context?

5:00 PM
Christian R Østergaard – Department of Business and Management, Aalborg University
Ina Drejer – Department of Business and Management, Aalborg University
The Role of Mobility and Employee-Driven Relations for University-Industry Collaboration on Innovation

5:20 PM
*Nina Hjertvikrem – University of Stavanger
Research Networks and Regional Differences in Innovation Activities

5:40 PM
*Ole Bergesen, PhD Research Fellow – University of Stavanger
Ragnar Tveterås, Professor – University of Stavanger
From Knowledge to Innovation: Firms´ Internal Skills, Collaboration Choices and Innovative Activity

6:00 PM
*Lukas Ernst Vogelgsang –
Open Routines in Creative Collaboration

Making Smart Communities: Streamlining Research, Development, and Deployment

Making Smart Communities: Streamlining Research, Development, and Deployment

by Jennifer Clark, Center for Urban Innovation

Jnnifer Clark US Energy and House Committee Image March 16, 2017 2

On March 16, 2017, I was invited by the US House Energy and Commerce’s Subcommittee on Digital Commerce and Consumer Protection to provide expert testimony about the importance of smart communities to commerce and infrastructure systems. The Committee held the “hearing to examine the ways that communities across the country are tapping into new technology and collaborating with private sector companies to deliver new initiatives that will improve safety, increase efficiency and create opportunity.”

My oral testimony at the hearing may be viewed on the Committee’s website and is part of the Committee’s “Disrupter Series” on emerging technologies. My full written witness statement is included in this blog post and also available on the Committee’s website.

Written Testimony on Smart Communities

Formal Citation: United States. Cong. House. Committee on Energy and Commerce. Subcommittee on Digital Commerce and Consumer Protection. Hearing on Disruptor Series: Smart Communities. March 16, 2017. 115th Cong. Washington: GPO 2017 (statement of Dr. Jennifer Clark, Associate Professor, Georgia Institute of Technology)


Smart communities are critical to the future economic competitiveness of the United States. Smart communities are not just an opportunity to increase economic growth but they present a challenge as well: Does the U.S. invest in intelligent infrastructure to build the 21st century economy and plan for what’s beyond?

The Federal Government has an important role to play in shaping the scope and scale of intelligent infrastructure investments going forward. In short, the Federal Government will decide the platform on which the national economy is built going forward and whether it meets 20th century standards or sets a new standard for the 21st century economy. Research universities have extensive experience partnering with industry and government on technology diffusion projects like smart communities. Research universities are built to test new technologies, evaluate alternatives, assess investments, evaluate economic impacts, measure distributional consequences, and certify processes, materials, products, and standards. As with any new enabling technology, research universities can play a role as a neutral third party with specialized technical expertise. Further, universities are embedded in local communities and have long-term working relationships with local and state governments and a vested interest in the presence of world class infrastructure in their own communities.

How to design and deploy intelligent infrastructure to efficiently and effectively support smart communities is one of the central questions going forward for the country as a whole and for local communities in specific. Building the replicable models and dissemination networks for the broad and sustained implementation of information and communication technologies into the next generation of national infrastructure is the opportunity and the challenge before us.


“Smart communities” have captured the attention of popular audiences and experts alike. The “Smart City” concept promises access and opportunity as well as expanded services and increased efficiencies for local communities. The idea promises simultaneously to generate new revenue via new markets, products, and services and to save money through new efficiencies and systems optimization.   Advocates argue that smart communities are more efficient, more sustainable, more profitable, and more inclusive.

Economic geographers have long studied innovation as part of the broader disciplinary project of mapping and analyzing the spatial distribution of economic activities within and across cities, regions, and countries.  In recent years technology and innovation have gained privileged positions of prominence in these industry analyses. Researchers particularly focused on processes of technology diffusion and how regional economic ecosystems absorb new technologies and incorporate them into existing complexes of firms, industries, and industrial specializations.  In other words, how incumbent systems incorporate new processes, products, materials, and actors.

Smart communities are a challenge and an opportunity for the U.S. The challenge is to proactively engage the declining, incumbent national infrastructure system and not merely repair it, but replace it, with an internationally competitive cyber-physical system which provides not only an opportunity for better services for citizens but a platform for a 21st century, high tech economy and beyond.

Smart Communities and US Economic Competitiveness

Smart communities are critical to the future economic competitiveness of the U.S. Over 90 percent of the country’s GDP is generated in metropolitan economies — in cities and their suburbs. Smart communities are not just an opportunity to increase economic growth and opportunity but they present a challenge as well: Does the U.S. invest in intelligent infrastructure to build the 21st century economy and plan for what’s beyond? Or, does the U.S. miss the moment when targeted investment in integrating information and communications technologies (ICT) into infrastructure systems could form the foundation of an “Industry 4.0” level cyber-physical systems. The state of U.S. infrastructure and amount of funding devoted to it undermines U.S. global leadership in smart communities innovation and implementation. The American Society of Civil Engineers’ latest report card ranked America’s infrastructure at a D+, requiring $3.6 trillion in investment. The question is how can the U.S. plan a smart communities future, and the research and development necessary to support it, when there is such a critical gap in incumbent infrastructure systems?

The economic opportunity presented by smart communities is three-fold. First, the data produced by intelligent infrastructure promises to increase the reliability of local government services and performance of infrastructure systems. The data paves the way for building interoperable and cross platform systems that build efficiencies and ultimately allow localities to provide higher quality services at a lower cost. The result is the opportunity to expand services and maintain more reliable and efficient systems ranging from waste management to transportation.

The second opportunity is that smart communities data systems can enhance and inform the strategic planning capacities of local communities — large and small — with real world (continuous and real time) data on how infrastructure and infrastructure systems are used by citizens and businesses and how the infrastructure is performing. Local communities, businesses, and citizens will be able to see how their community is operating rather than model its functions based on past performance.

Further, the sharing of data amongst smart communities partners and participants helps to build networks for diffusing policy strategies and technology models. These strategic partnerships form the foundation for the third economic opportunity that flows from smart communities: entrepreneurship and market leadership. The data generated by and for smart communities systems (and the systems that produce that data) form the foundation of new enterprises and new products and services and, as a consequence, function as platforms for further economic development.

“Intelligent Infrastructure”: Next Generation Services and Structures

The promise of “smart” or “intelligent” infrastructure is that it will increase resilience across domains of critical infrastructure systems by expanding capacities and building resiliency through increased interoperability. In other words, by moving from a collection of discrete infrastructure systems to truly interdependent infrastructure ecosystems, the efficient, effective, predictable, and adaptive delivery of services will increase as well.

Across disciplines ranging from engineering to computer science to innovation policy, intelligent infrastructures are increasingly seen as solutions to the “wicked” problems that face local governments. These problems include how to respond to both long term and short term threats to resilience: 1) strained resources spread across ever growing urban populations, 2) aging infrastructures and public services systems, 3) competitiveness in the global economy, and 4) acute human and environmental stressors.

In recent years, governments ranging from dense urban environments to rural communities have made significant investments in smart and connected communities (SCCs), leveraging the capacity of information and communication technologies (ICTs) to improve existing operations and develop new services. The resulting “intelligent infrastructure” is dependent on a layer of new technologies to collect and store data, combine data from both fixed and mobile sensing devices, integrate existing data sets, and report the status of the city to user groups including businesses, governments, and communities. These new data streams come from connected, self-reporting, sensing devices (e.g. the Internet of Things, or IoT), citizen contributions (e.g. crowdsourcing), and municipal and official sources (e.g. open government data). These new capacities contribute to an increasingly complex system of users, platforms, interests, and information—with profound implications for systems design and governance.

This infrastructure presents particular challenges because it is integrated both into and across different critical infrastructures. From water and electricity systems and across built, natural, and socio-economic environments, robust intelligent infrastructure is increasingly required for the secure and resilient operations of government services and systems. As a consequence, this infrastructure-of-infrastructures presents a unique problem for critical infrastructure: how to integrate the capabilities and capacities of intelligent infrastructure into incumbent systems while mitigating interruptions, reducing exposure to threats, and ensuring continuity of service? In short, intelligent infrastructure requires attention in its own right as a new critical public infrastructure.

Intelligent infrastructure is quickly becoming central to the operations of critical infrastructure providing services ranging from water, to energy, to multi-modal transportation, to health, to communications. And, economic competitiveness is increasingly tied to the reliability and resilience of these critical infrastructure. Simply put, places without robust intelligent infrastructure systems will be left behind in the global economy because their critical infrastructure systems — utilities, energy, transportation, health, and emergency services — will be not be competitive compared to places who made the investments in cyber-physical systems to support operations.

Intelligent infrastructure directly impacts the management of systems through manual and semi- and fully-autonomous interventions, such as allowing changes to traffic lights during a period of heavy vehicle throughput. Intelligent infrastructure also indirectly impacts existing systems by providing information important to design, maintenance, and decision-making from operations to city planning and administration.

The products currently emerging in the context of smart communities are largely service-embedded goods built on a platform of critical infrastructure systems. In other words, smart communities cannot move forward without intelligent infrastructure. Smart communities require: 1) connectivity (reliable, predictable, interoperable, and upgradeable), 2) analytical services (expertise and assets to make data legible and useable), 3) data storage and management services (including security and privacy), and 4) open access to data through platforms and interfaces for citizens, entrepreneurs, and incumbent firms to build enterprises and expand engagement.

For example, a “smart cities object” — a trash can, a streetcar, a light pole, a traffic light — requires embedded sensors. Those sensors require connectivity (fiber, wireless, etc.). The object requires a service contract to maintain and manage that connectivity. Data analytics are required to manage the resulting data and perform analysis. Interfaces and visualization tools are required to make the data accessible to citizens and businesses. Smart communities are a market-making enterprise and failing to invest in intelligent infrastructure misses the opportunity to provide local communities with globally competitive roads, bridges, and transit but also abdicates the opportunity to build a new industry around the products, services, and systems developed on the platform of intelligent infrastructure.

Making Smart Communities: Streamlining Research, Development, and Deployment

The making of smart communities follows a model of technology diffusion familiar in the private sector context. This, however, is technology diffusion into a public-sector context where there is a necessary focus on the broad provisioning of reliable and efficient services and a consideration for building access to data for enterprise development. There are significant private sector participants in smart communities and some of these firms have created consortiums to offer communities integrated and interoperable packages of hardware, software, and connectivity services.

In the U.S., the national innovation system largely relies of publicly-funded basic research and development conducted within the network of world class research universities throughout the country. For decades, these universities have served as the research and development backbone of U.S. industry and of national defense. Research indicates that this national innovation has been effective in bringing forward new technologies and in facilitating the commercialization of new products, processes, and materials.

In the smart communities context, research universities are again serving an essential role in the research and development phase of smart communities innovation. At Georgia Tech, we are engaged in developing new policy models for smart communities as well as new technologies including data analytics, sensor networks, and operating systems. Through this research we have identified four key elements in smart communities technology projects: 1) Phased technical deployment to increase opportunities for in-action learning, community engagement and responsiveness, and integration of ongoing technical improvements, while simultaneously reducing the implementation burden on participating organizations, 2) Comprehensive administrative and technical strategies focused on interoperability that account for the necessary current and future need for systems to communicate and foster expansion over time, 3) Programmatic commitments to engaging the community at large, and to integrating concerns originating in everything from planning to technical specifications in meaningful ways and tailored to local conditions, 4) Established policies around open data and open innovation in order to ensure both continued access and local and regional economic development.

Local governments are focused on managing growth and change in their communities and providing services to citizens. Rarely do local governments have internal research specializations. Although some larger local governments have made recent investments in innovation delivery teams, information management teams, and resilience offices, these efforts remain focused on enhanced service delivery to citizens. Further, many of these efforts have been financed by philanthropic investments by leading national foundations interested in improving the quality of life and capacity for service delivery in local communities. In other words, even the exemplar smart communities programs are largely experiments with limited resources, limited timelines, and unclear scalability.

Research universities have extensive experience partnering with industry and government on technology diffusion projects. Research universities are built to test new technologies, evaluate alternatives, assess investments, evaluate economic impacts, measure distributional consequences, and certify processes, materials, products, and standards. As with any new enabling technology (biotechnology, nanotechnology, advanced manufacturing, photonics) research universities can play a role as a neutral third party with specialized technical expertise. Universities are also embedded in local communities and often have long-term working relationships with local and state governments. Research universities also have vested interest in the upgrading and maintenance of intelligent infrastructure in the cities and communities in which they are located. World class industry partners, star scientists, and the next generation of entrepreneurs all look for intelligent infrastructure to support their research and commercial enterprises. The absence of this infrastructure makes universities less globally competitive — for talent and for capital. And, as stated before, such absences make local communities less globally competitive as well.

Rather than stand up research and development divisions in every local government in the country in order to assess and deploy smart communities technologies, it would be reasonable to again turn to the nation’s network of world class universities, like Georgia Tech, to conduct the research and development work of smart communities and thus facilitate the path to deployment by local communities.

Finally, as research universities train the next generation of workers, citizens, and entrepreneurs, it is important to recognize that living and working in smart communities will be distinct from the built environment in which we live now. Whether the changes are immediately disruptive like autonomous vehicles or incremental adjustments to the skills required for living in and navigating the built environment (think automated grocery store check outs, smartphone based parking systems), investments in technical training for new and incumbent workers will be required to take advantage of the value-added these technologies bring to the labor market. Universities again will be critical partners in developing both these technologies and the skilled workforce required to capitalize on their contributions to national and regional growth.

Smart Communities Implementation and the Role of the Federal Government

In 2015 the U.S. Department of Transportation announced a Smart Cities Challenge for cities across the country. The competition was a “winner take all” grant which Columbus, Ohio won. But 77 other communities also applied for the grant. In other words, 77 local communities across the country pulled together strategic plans for implementing intelligent infrastructure systems in their own communities and tailored to their own needs. The Federal Government has long played an essential role in investing in infrastructure and in emerging technologies. Smart Communities combine both these roles. And communities across the country have demonstrated their readiness to move forward.

The Federal Government has several key roles going forward. First, as noted above, smart communities involve technology diffusion into a complex private sector and public sector space — and that space is also a place, a jurisdiction. The implementation of smart communities involves engaging real people in real places in real time. Therefore, flexibility and policy tailoring will be essential to successful implementation. What works in New York City is unlikely to be exactly what works in Columbus or Savannah or Dallas. One size will not fit all.

Although the Federal Government should not set a standardized approach, the Federal Government should consider developing technical standards and platforms for data, connectivity, and integration of hard infrastructure and information and communication technologies to protect citizens and consumers from excessive experimentation. The National Transportation Safety Board’s approach to guidance on autonomous vehicles is a good example of signaling to industry, local governments, and researchers about how to shape strategic planning and private investment while protecting consumers and citizens. The National Institute of Standards and Technology’s efforts to develop the global cities team challenge and convene industry, local governments, and universities to discuss and develop standards is an important start as well.

Because smart communities technologies cut across domains they also do not fit neatly under a specific federal agency. Many of the efforts to consider and support smart communities have been partial and ad hoc. The recent call for public comments by the Networking and Information Technology Research and Development (NITRD) Program on the “Smart Cities and Communities Federal Strategic Plan: Exploring Innovation Together” is a start at coordinating planning across the Federal Government.

Georgia Tech and the City of Atlanta are partners in a national network designed for developing smart communities policies and technologies with the scalability of those models to other local governments in mind. The MetroLab Network is a network of 38 cities, 4 counties, and 51 universities, organized into “city (or county) – university partnerships” focused on “research, development, and deployment” (RD&D) projects that offer technologically- and analytically-based solutions for challenges facing communities: mobility, security and opportunity, aging infrastructure, and economic development. One role for the Federal Government is in resourcing and institutionalizing these networked partnerships to support policy diffusion across communities and information exchange about how smart communities investments (programs, projects, and objects) perform as implemented. These networks allow local governments to achieve some economies of scale, build capacity, and avoid replicating mistakes or reinventing the wheel.

The Federal Government has an important role to play in shaping the scope and scale of intelligent infrastructure investments going forward. Simply put, the Federal Government will decide the platform on which the national economy is built going forward and whether it meets 20th century standards or sets the standard for the 21st century. There is a significant amount of basic research required to ascertain how to achieve the promise of smart communities. Some of that research can be resourced through programs like the Smart and Connected Communities program or the Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) program of the National Science Foundation. However, the current resources are modest investments in basic research and not of a sufficient scale to support the broad, national technology deployments necessary.

There is also a significant amount of applied research required to move smart communities technologies from design to development to deployment. There is a growing need for education and training. In research universities like Georgia Tech we are developing new curriculum to integrate teaching and learning about innovation and communities, technology and cities and regions. We are also investing in research centers, like the Center for Urban Innovation and the Institute for People and Technology, that take an interdisciplinary approach to moving innovations in engineering, sciences, and computing into a complex real world context of communities, entrepreneurs, and industries. How to design and deploy intelligent infrastructure to efficiently and effectively support smart communities is one of the central questions going forward for the country as a whole and for local communities in specific. Building the replicable models and dissemination networks for the broad and sustained implementation of information and communication technologies into the next generation of national infrastructure is the opportunity and the challenge before us.

Teaching Smart Cities: From Urban Policy to Urban Innovation

by Jennifer Clark, Center for Urban Innovation

A Sample Smart City from IDC Government Insights (2013), courtesy Smart Cities Council

The topic of smart cities — as a discourse and as a practice — came on the popular scene first with initiatives such as IBM Smarter Cities in the early 2010s and has since captured a much wider audience. Like many technology projects, smart cities have caught the public imagination as something novel. Self-driving cars are presented as “disrupting” transportation models and the built environment itself. And yet, self-driving cars are still just individual cars. They drive on the same streets that have defined the urban form for more than a century. They may influence the demand for parking but it is less clear what effect they will have on roads. If anything, such technology appears to be incremental, not disruptive. And, when policy expertise enters the conversation, we see the clear evidence of this obvious incrementalism.  

The growing interest in smart cities has presented some interesting questions to the academic community: Where does one learn about smart cities? Who teaches smart cities? What discipline or degree programs prepare students to design, implement, and evaluate smart cities?

“Smart cities” is rarely seen for what it is — a technology diffusion challenge operating in a dynamic and contested space between the public and the private sector.  The technology development will likely prove to be the easy part; it is the design and deployment of these models into this liminal space where governance, regulation, access, participation, and representation are all unclear and the “operating standards” are yet to be fully articulated that will prove to be the real challenge.

Smart cities present a very interesting challenge to teaching and to curriculum development in universities. This is a technology-intensive field which is fundamentally interdisciplinary and necessarily rooted in the social sciences. What makes cities are people — the choices they make, the places they go, the things they buy, and where they live and work. The built environment shapes those choices and urban systems facilitate or aggravate both movement across and living in cities. But at their core, cities are complex political, economic, and social systems. So, the challenge of smart cities is not one of technology alone. Indeed, most of the relevant technologies exist and currently operate in other contexts like manufacturing and defense. The question then becomes — beyond a grasp of the underlying technologies — what does one need to know to be a smart cities expert?

What are the prerequisites for studying smart cities? Does it require a background in data analytics? Civic computing? Civil engineering? Or, does the mastery of smart cities require knowledge of cities themselves? Stated another way, could you effectively study biotechnology without mastering organic chemistry or biology? Could you study astrophysics without an understanding of physics and mathematics?  

I began teaching university-level courses about how to study cities in 2004 at Cornell University. The first course I taught was an introduction to urban fieldwork tailored to undergraduate urban studies students. The course was intended to prepare students for careers that required understanding the actors and processes that shape the urban environment.  

Since then, I have taught many other courses on urban policy and urban and regional economic development at Georgia Tech. I have also coordinated a graduate concentration of the MSPP degree in public policy specializing in urban policy and anchored by a two semester course sequence PUBP 6604: Urban Policy Analysis and Practice and PUBP 6606: Urban Development Policy. And, in my experience, every year these courses change at the margins if not in their core content. These courses change because cities themselves are dynamic — what cities do and why and how changes over time and thus, so does the study of them. After teaching these courses for more than a decade, I see them now through the lens of the evolution of the field itself from urban policy to urban innovation.

The evolving nature of both the discipline and the practice has been highlighted to me through my evolving use of the two core books I have taught for several years in Urban Policy Analysis and Practice: 1) Basic Methods of Policy Analysis and Planning (a book I co-authored with colleagues in policy and planning disciplines), and 2) Fast Policy (a book co-authored by colleagues from urban and economic geography). Both books emphasize the speed at which policy analysis and policy diffusion occur and the role of institutions and analysts in speeding along policy change — and their corresponding responsibilities in slowing it down — to be more deliberate, assess alternatives, and make informed determinations about what works and what doesn’t and for whom. In other words, the need for urban innovation experts to understand efficiency, equity, distribution, and impact in addition to technology. Fundamentally, smart cities are about being smart, not just being high-tech.

In February 2016, the President’s Council of Advisors on Science and Technology (PCAST) released a major report “Technology and the Future of Cities.” The report outlined a strategy to guide federal investment and engagement in smart cities initiatives. Although the future of these initiatives and the impact of the original PCAST report in influencing investment is uncertain, the report itself revealed some interesting absences. Only a small number of the more than 100 contributors to the Future of Cities Report represented the perspective or expertise of the social sciences focused on cities and the urban scale: urban policy, urban planning, urban geography, urban history, urban economics, or urban administration.  

Historically, the array of social science fields focused on cities are sub-fields of much larger disciplines — economics, political science, geography, history. After decades of deindustrialization and disinvestment in cities, these sub-fields are not always the most popular or publicized. However, urban planning — to varying degrees — is the exception to the sub-field rule. Within urban planning, the consensus opinion has long been that urban planning is a discipline of its own. Its disciplinary boundaries run parallel to architecture in that there is a core curriculum, a professional master’s degree, professional certifications, and a clear professional practice. One is trained as an urban planner to work in urban planning. In other words, urban planning has rarely identified as an interdisciplinary project.  

As a consequence, “smart cities” as a domain, has emerged into the world of degrees and disciplines in which its home is likely to be fluid rather than fixed. Teaching smart cities will likely be a collaborative and interdisciplinary project with its core knowledge claims rooted in an understanding about the workings of cities and its novel value claims oriented around its interdisciplinarity and its integration of knowledge about not just technology but how technology can be used in the urban context.

For me and the curriculum I teach, the promise of urban innovation is exciting. I look forward to teaching urban policy as the landscape changes and smart cities becomes a centerpiece of investment and administration. Cities have never stood still. There is no reason why the curriculum about them should either.

People-Centered Planning in Smart Cities

By Emma French

The term “smart city” has become common parlance in city planning circles in recent years. While there is no universally agreed upon definition, descriptions of smart cities typically refer to integrated and interoperable networks of digital infrastructure and information and communication technologies (ICT) that collect and share data and improve the quality of urban life (Allwinkle and Cruickshank 2011; Batty et al. 2012). However unlike related concepts such as the digital city, the intelligent city and the ubiquitous city, the smart city is not limited to the diffusion of ICT, but also commonly includes people (Albino, Beradi, and Dangelico 2015).

Many of the technological enhancements propelling the smart city revolution require re-designing and in some cases re-building the underlying infrastructure that holds cities together. City planners will therefore play a significant role in the creation and implementation of many smart city initiatives. In a 2015 report on smart cities and sustainability, the American Planning Association (APA) purported that new technologies will aid planners by creating more avenues for community participation in policy and planning processes (APA 2015).

Public Participation in Planning

Widely-held conceptions of planning have shifted over the last century from normative, rational models that position planners as technical experts, toward a theoretical pluralism characterized by the political nature of planning, competing interests of stakeholders, and decisions as negotiated outcomes facilitated by planners (Lane 2005). These more contemporary models, most of which were first conceptualized in the 1960s and 1970s, view citizen participation as a key part of the planning process. Smith (1973) argues that participatory planning increases the effectiveness and adaptability of the planning process and that citizen participation strengthens our understanding of the role of communities in the urban system.

Meaningful public participation in planning has been found to better planners’ understanding of the community context (Myers 2010), improve decisions through knowledge sharing (Creighton 2005), increase trust in political decision making (Richards, Blackstock, and Carter 2004; Faga 2010), produce long-term support of plans (Levy 2011), enhance citizenship (Day 1997; Smith 1973), build social capital (Layzer 2008), and address complex problems through collaboration and consensus (Innes 2010; Godschalk 2010).

While these more contemporary planning models emphasize the importance of citizen engagement, achieving meaningful participation has proved difficult. Challenges preventing meaningful citizen participation include funding and resource constraints (Creighton 2005), literacy and numeracy (Community Places 2014), disinterest (Cropley and Phibbs 2013), lack of access to necessary resources (Cropley and Phibbs 2013), the prescriptive role of government (Njoh 2002), power inequalities within groups (Reed 2008), jurisdictional misalignment (Layzer 2008), and lack of respect for public opinion (Day 1997).

17-2-24In her seminal 1969 article, A Ladder of Citizen Participation, Arnstein uses examples from
federal urban renewal and anti-poverty programs to illustrate different manifestations of participation in practice (see figure to the right). Arnstein defines citizen participation as “the redistribution of power that enables the have-not citizens, presently excluded from the political and economic processes, to be deliberatively included in the future” (
Arnstein 1969, 216). Arnstein’s examples show how some efforts to include citizens in planning and decision making can perpetuate existing systems of power and actually further disenfranchise marginalized communities.

Glass (1979) attributes the dearth of meaningful citizen participation in planning and policy making processes to lack of attention to the design of participatory programs and a mismatch between objectives and techniques. Glass concludes that if the goal is just to get citizens to participate then almost any technique will be seen as sufficient. He argues that one technique alone is never enough and that meaningful citizen participation requires a continuous, multifaceted system of engagement (Glass 1979).

Technology-aided Participation

For decades scholars have been exploring ways that technology can enable meaningful participation in planning and policy making. Recent hype around “smart cities” has fueled the debate about the role of technology in these processes. Technology has been found to support citizen participation in planning by increasing participants understanding of issues and proposed plans (Salter et al. 2009), supporting collaboration (Jankowski 2009), strengthening the role of low-income residents (Livengood and Kunte 2012), and enabling alternative, informal manifestations of civic engagement (Asad and Le Dantec 2015).

Simply adding technology to the planning equation, however, does not always guarantee meaningful participation (Sylvester and McGlynn 2010; Epstein, Newhart, and Vernon 2014; Holgersson and Karlsson 2014). While the use of technology may address some barriers to participation in planning processes, it may actually exacerbate other barriers that stem from structural social, economic and environmental inequities.

Equity, Planning and Smart Cities

Despite the emphasis of meaningful citizen participation in planning, low-income, urban communities of color often still suffer from poor infrastructure, environmental degradation and exposure to toxins, and potential displacement due to rapid gentrification. A concern voiced by many critics of smart cities is that, like previous attempts to use technology to engage the public, the existing digital divide will likely limit use of smart city technologies to certain groups of people with certain resources and skills.

Using 2007 Pew survey data, Sylvester and McGlynn (2010) conducted four logistical regression models that try to explain the factors leading to individuals having “low access” to the Internet and how internet usage and physical location influence civic participation. They find that living in a rural area and being African American or Hispanic increase the probability that you will have low access to the Internet. Age was found to have a significant, negative effect on Internet access—meaning that the younger you are the more likely you are to have access to the Internet. The results also showed that people living in urban areas were more likely to contact the government by phone (Sylvester and McGlynn 2010).

The recent hype around smart cities is fueled to some degree by the rapid migration of people into cities. In 2014 ,fifty-four percent of the world’s population lived in urban areas and the World Health Organization estimates that by 2030 that number will be closer to eighty percent (WHO 2017). Atlanta is expected to grow by about 2.5 million people in the next 25 years; however, income inequality in the city is increasing and poor urban residents are being displaced by millennials and baby boomers (Coleon 2016).

This brings up a major concern regarding smart cities. Namely, who are we making cities smart for? If our efforts to make cities more efficient, safe, and clean result in the displacement of marginalized communities, are these cities really smarter than the ones in we live in now? No sensor can substitute for public engagement and responsive leadership. Agyeman and McLaren (2016) advise against the creation of tech hubs without a simultaneous strategy to protect and invest in affordable housing, basic services, and infrastructure.  

Adam Greenfield presents a similar, albeit more in-depth, critique in Against The Smart City, where he investigates three major international smart city urban developments and argues that the marketing materials and promises of the sponsors highlight their interest in this top-down, data-rich urban management system (Griffiths 2013).

The Role of Planners in the Smart City

In the APA’s Smart City and Sustainability Task Force survey, planners ranked socio-economic disparity as the second most important topic for planners working in smart cities (after green building and site design), suggesting that planners are aware of the importance of socio-economic stratification. But what can planners do to ensure that investments in smart city technologies are benefiting everyone equally, rather than sucking away financial and political resources needed to fix basic infrastructure issues? How can planners use these technologies to support more meaningful community engagement?

The existing literature suggests that even where technologies enable greater understanding of the planning issues or more meaningful engagement, they must be used in tandem with of traditional modes of planning such as in person meetings and design charrettes. Scholars also emphasize the need for ongoing, participatory mechanisms. Especially where institutionally-mediated participation falls within the first five rungs of Arnstein’s ladder, perhaps ICTs can play a role in supporting alternate, illegitimate forms of civic action that have a greater impact.