Through a unique combination of data science and legal analysis techniques, the National Zoning Atlas is creating the first public, online repository of standardized data about zoning. This article first discusses the context for and methodology behind the atlas. It then establishes three possibilities for
using the atlas, including facilitating research (including fair housing research), strengthening planning, and empowering the public.

This article analyses the regional variation in outcomes of a seemingly standardized federal neighborhood valuation principle used in home mortgage insurance grading. The objective is to highlight the contingent discriminatory and economic conditions that mediated heterogeneous housing outcomes across different parts of the United States. How did city and regional economic and demographic growth patterns vary before and during the mortgage insurance program implemented through the Federal Housing Administration (FHA)? How may this have shaped loan guarantee patterns? How does pre-existing racial housing discrimination relate to outcomes? Adopting an orientation that centers on Whiteness and the benefits of mortgage finance for certain groups and neighborhoods, this analysis uses a Bayesian hierarchical framework to investigate the degree of the FHA’s influence between 1940 and 1970, here proxied by the Home Owners’ Loan Corporation maps, on A or B (“AB”) graded neighborhoods versus C graded neighborhoods in different cities. This article studies how home values and homeownership changes over time and whether there regional variation in the influence of these grades. It also studies what longitudinal socioeconomic patterns might explain the persistence or decline of the AB effect over time. Findings show cities in the West Coast, Southwest, and Northern Central United States that saw the most housing construction also had the highest proportions of FHA loans to overall dwelling units. There is also a distinctive consistency and persistence of benefit on home value and homeownership to AB graded neighborhoods in these cities, possibly owing to regional shifts in the industrial landscape.

This research uses high-density anonymized mobile phone application (MPA) global-positioning system (GPS) data to describe exposure to racial diversity in different social contexts with an aim to clarify the mechanism linking residential diversity to opportunities for diverse social interactions. In particular, it explores the hypothesis that a diverse residential context does not lead to diverse social contact by comparing three exposure measures – residential, observed and interaction – on the census block group level in Chicago. In doing so, it also explores the contribution of activity spaces to opportunities for diverse social contact. The findings show that the exposure to opportunities for diverse social contact measured by MPA data is generally higher than what is implied by residential census data, especially in areas of high residential segregation in the city. Further, measures using MPA data reveal more spatiotemporal heterogeneity of exposure than that implied by the residential context.

How did institutionalized discriminatory lending policies implemented under the guidance of the Federal Housing Administration (FHA)’s mortgage risk maps impact neighborhood trajectories? Have these spatially restrictive credit designations influenced home value, homeownership, and racial segregation? Using the FHA mortgage risk map of Chicago, Illinois, for new loan guarantees as a case study, I measure outcomes between credit zones and compare these risk regions with the Home Owners’ Loan Corporation (HOLC) Residential Security Maps, which represent post hoc measures of mortgage risk and were likely not directly used in loan activities. For areas excluded from FHA loan guarantees, the results suggest a negative impact on home values and homeownership rates and weakly decreased segregation between 1940 and 1980. They also suggest an overcorrection of home values, an under correction of homeownership, and an increase in racial segregation in excluded neighborhoods between 1980 and 2010 when these areas may have experienced capital reinvestment. In comparison with the HOLC map, the effects on tracts in Chicago rated worst by the FHA are clearer and suggest a more significant impact during the period of discriminatory mortgage lending.

“Ghost Cities” have become a phenomenon of global interest since 2009 when popular media highlighted the existence of Ordos, a large Chinese city that was almost entirely vacant. The term is used to describe housing vacancy associated with overdevelopment and can refer to small communities, neighborhoods, or even whole cities that lie vacant. Ghost Cities are particularly prevalent in China where housing vacancy has become a serious concern for many second and third-tier cities. Measuring the extent of these vacant areas has been challenging due to Chinese data restrictions. This research tests whether it is possible to collect data, scraped from Chinese social media open access API's including Dianping (Chinese Yelp), Amap (Chinese MapQuest), Fang (Chinese Zillow), and Baidu (Chinese Google Maps) to develop a computational model to identify areas considered to be Ghost Cities. The model created for this study is based on the idea that thriving communities need access to basic amenities. Therefore Hansen's gravitational model was applied to give an “amenities score” for residential locations based on their accessibility to restaurants, banks, grocery stores, beauty salons, KTV, medical facilities, schools, and malls Moran's I spatial autocorrelation was applied to the amenity scores below the mean to determine spatially clustered residential locations with low scores. The results were considered potential Ghost Cities and were visited in Chengdu and Shenyang to confirm the accuracy of the model. These site visits showed that the model identified transitional, underperforming, or vacant housing in these cities, illustrating that it is possible to use data scraped from social media to identify underused residential developments.

The field of urban analytics and city science has seen significant growth and development in the past 20 years. The rise of data science, both in industry and academia, has put new pressures on urban research, but has also allowed for new analytical possibilities. Because of the rapid growth and change in the field, terminology in urban analytics can be vague and unclear. This paper, an abridged synthesis of a panel discussion among scholars in Urban Data Science held at the 2019 American Association of Geographers Conference in Washington, D.C., outlines one discussion seeking a better sense of the conceptual, terminological, social, and ethical challenges faced by researchers in this emergent field. The panel outlines the difficulties of defining what is or is not urban data science, finding that good urban data science must have an expansive role in a successful discipline of “city science.” It suggests that “data science” has value as a “signaling” term in industrial or popular science applications, but which may not necessarily be well-understood within purely academic circles. The panel also discusses the normative value of doing urban data science, linking successful practice back to urban life. Overall, this panel report contributes to the wider discussion around urban analytics and city science and about the role of data science in this domain.

One of the most dramatic shifts in housing systems occurred during the 1980s and 1990s, when China transitioned from a socialist, state-led housing system toward as more market-based housing market. This transition occurred in tandem with broader economic reforms in the 1980s and 1990s that transformed China’s centrally planned, socialist economy to a competition-driven and market-oriented one.

By examining the relationship between housing reforms, and resultant housing construction and household residential location choices respectively, this chapter speaks to the first theme of this volume, which focuses on how urbanization in China has been shaped by state interventions and associated social, economic, and physical interactions. Specifically, we ask,: Did each successive wave of construction after major urban housing reforms in 1998 contribute to more expensive residential housing units being more closely clustered together and located further away from lower-priced housing units?

How do the people exercise power? For West Side Chicago residents in the 1960s who were part of Organization for a Better Austin (OBA), the exercise of power needed to be opportunistic and confrontational. To pressure banks to reinvest in their community, they took action: they scattered hundreds of pennies in the lobby of a bank lender, so as to disrupt business, and they surrounded a blockbusting real estate agent in the basement of a house until the agent promised to take his business out of the neighborhood.