John Snow in 1849 proposed the intestinal fecal-oral theory for cholera and provided substantial evidence supporting the theory and particularly the prediction that cholera was water-borne. Snow’s analysis fits naturally within a Neyman-Rubin causal framework, and Snow is credited with two tools (randomization as an instrumental variable and difference-in-differences design) widely used in causal analysis today. Nonetheless, although water was widely accepted in the 1850s as a causal factor, Snow’s theory was not. This seeming puzzle cannot be resolved within the Neyman-Rubin framework and requires a broader conception of abductive scientific inquiry, as advocated by Peirce (and recently Heckman and Singer 2017). The methodology of scientific research programmes of Lakatos (1980) provides a concrete framework for such a broader conception of scientific inquiry, and for understanding the competition among theories in the 1850s. A “rational reconstruction” of the case of cholera illustrates the logic of inquiry: alternative theories (rationally) protected against refutation by incorporating water as a causal factor, but Snow’s theory was superior (progressive in Lakatos’s terms) – producing new predictions corroborated by new facts. Besides illustrating how the abductive process of scientific inquiry works with an important history of science example, we hope to encourage quantitative social scientists, especially those who rely on potential outcome frameworks, to see their research in this larger context. Finally, this is part of an effort to support teaching statistical data analysis as part of the broader process of scientific inquiry. [237 words] University of Chicago, Harris Public Policy 1307 E 60th St. Suite 3037 Chicago IL 60637 203-252-4897

Nonmedical opioid (NMO) use has been linked to significant increases in rates of NMO morbidity and mortality in non-urban areas. While there has been a great deal of empirical evidence suggesting that physical features of built environments represent strong predictors of drug use and mental health outcomes in urban settings, there is a dearth of research assessing the physical, built environment features of non-urban settings in order to predict risk for NMO overdose outcomes. Likewise, there is strong extant literature suggesting that social characteristics of environments also predict NMO overdoses and other NMO use outcomes, but limited research that considers the combined effects of both physical and social characteristics of environments on NMO outcomes. As a result, important gaps in the scientific literature currently limit our understanding of how both physical and social features of environments shape risk for NMO overdose in rural and suburban settings and therefore limit our ability to intervene effectively. In order to foster a more holistic understanding of environmental features predicting the emerging epidemic of NMO overdose, this article presents a novel, expanded theoretical framework that conceptualizes "socio-built environments" as comprised of (a) environmental characteristics that are applicable to both non-urban and urban settings and (b) not only traditional features of environments as conceptualized by the extant built environment framework, but also social features of environments. This novel framework can help improve our ability to identify settings at highest risk for high rates of NMO overdose, in order to improve resource allocation, targeting, and implementation for interventions such as opioid treatment services, mental health services, and care and harm reduction services for people who use drugs.

Since its introduction more than 15 years ago, the GeoDa software for the exploration of spatial data has transitioned from a closed source Windows‐only solution to an open source and cross‐platform product that takes on the look and feel of the native operating system. This article reports on the evolution in the functionality and architecture of the software and pays particular attention to its new implementation as a library, libgeoda. This library, through a clearly structured API, can be integrated into other software environments, such as R (rgeoda) and Python (pygeoda). This integration is illustrated with two small empirical examples, investigating local clusters in a historical London cholera data set and among socioeconomic determinants of health in Chicago. A timing experiment demonstrates the competitive performance of GeoDa desktop, libgeoda (C++), rgeoda and pygeoda compared to established solutions in R spdep and Python PySAL, evaluating conditional permutation inference for the Local Moran statistic.

Background | Much of spatial access research measures the proximity to health service locations. We advance this research by focusing on whether health service funding is within walkable reach of neighborhoods with high hardship. This is made possible by a new administrative data source: financial contracts data for those human services that are delivered by nonprofits under contract with the government. Methods | In a prototypical spatial access study we apply a classic 2-step floating area catchment model for walkable network access to analyze 2018 data about contracted nonprofit health services funded by the Chicago Department of Public Health (CDPH). CDPH collected the data for the purpose of this study. Results | We find that the common container approach of aggregating contract amounts by provider headquarter locations in a given area (ignoring satellite service sites) underestimates the share of funding that goes to Chicago neighborhoods with higher hardship. Once service sites and spatial access are taken into account, a larger share of CDPH funds was found to be within walkable reach of Chicago’s high hardship areas. This was followed by low hardship areas (which could be driven by more headquarter locations there that do serve areas throughout thecity). Medium hardship areas trail both, perhaps warranting closer attention. We explore these results by program type and neighborhood with a spatial decision support system developed for the health department. Conclusions | The typical approach for analyzing human service contracts based on headquarters is misleading -- in fact, we find that results are reversed when service sites and walkable access are taken into account. This prototype provides an alternative framework for avoiding these misleading results.

This article introduces a new open software environment to support the measurement of a range of accessibility indices at scales going from the local to the national. In practice, the use of such indices has been impeded by the lack of open resources and the computational burden associated with large scale analyses. The environment consists of three parts: a new package, access, as part of the Python-based PySAL Spatial Analysis Library, a user-friendly point-and-click web implementation of the access computations, and support for the calculation of large-scale travel cost matrices, including a set of pre-computed origin-destination distance matrices for all the census tracts in the U.S. and census blocks in the 20 major cities. All three elements are open source and free to use. After motivating the development of the software environment, and situating the problem of access measurement in the literature, we briefly describe six commonly used access metrics. We then discuss in more detail the three important components of our software infrastructure. We close with an empirical illustration pertaining to access to health care providers, comparing the approach in the package to that taken in the web application.

Purpose Increasingly complex societal challenges call for new, innovative solutions that social hybrid business models can provide. Social supermarkets (SSMs) are one example offering access to affordable food to people living in poverty while reducing food waste of nearby retailers. Finding the “right” location is an essential part of this retail marketing strategy. However, limited research has attempted to investigate the specific conditions of locational planning for hybrid and nonprofit retail organizations. This paper illustrates the case of Austria where SSMs are well established. Design/methodology/approach A GIS-based white space analysis was carried out to identify potential neighborhoods or rural areas for new social supermarkets with sufficient nearby demand, supply and no existing SSMs. The empirical parameters for this spatial analysis can be transferred to European countries with similar ecosystems. The authors collected a unique data set of 79 (2014) and 88 SSMs (2019) and 4,665 (2014) and 4,211 (2019) food retailers as (potential) suppliers to SSMs. To determine demand, the authors relied on small-scale integrated wage and income tax data and unemployment rates (2011) from Statistics Austria. Findings Overall, Austria has very good spatial access to grocery stores, including to SSMs. SSM access increased especially in the capital of Vienna between 2014 and 2019. The GIS-based white space analysis identified several other regions where residents have a high demand for affordable food with sufficient potential suppliers of surplus food but no SSM yet. Neighborhood-level findings are released as part of a publicly accessible spatial decision support system. Originality/value The methodology allowed a specific definition of the key areas of relevance by matching the demand for SSMs, calculated as the number of people with low incomes in the respective regions in Austria, with the supply of SSMs, calculated as the amount of potential food loss prevention by nearby retail stores. These parameters have proven to help in identifying the white spaces and therefore can be used in Austria and other European countries with similar ecosystems.

Each year, government spends more than a trillion dollars in combined federal, state, andlocal funds to support hundreds of thousands of local service providers in a highly decentralizedsystem of human service provision in the U.S. – making it hard to gain a bigger pictureperspective on whether these combined funds go to where needs are concentrated. In light of thepromise of data-driven decision making and government open data, this article provides ablueprint to assess how ready open administrative data are for gaining such a perspective. As thecase example, we analyze 2017 open financial contracts data for human services funded byChicago, Cook County and Illinois to identify spatial access gaps to these services (with fundingamounts). We conclude that these data are not yet ready for such an analysis and outline 5 majordata gaps, including a lack of data on service site locations and fiduciaries. What we find is thatthe institutional fragmentation of human services contracting leads to a multitude of disparatedata sources, unstandardized data definitions and non-systematic coverage of key information onhuman services that need to be addressed in order to gain a perspective of where funding goesacross departments and jurisdictions. We outline what data elements are still needed to close theidentified gaps and point to a spatial access prototype we developed for the Chicago Departmentof Public Health (CDPH) for new data it collected to address several of these gaps.

Abstract. Methods for exploratory data analysis and exploratory spatial data analysis (or ESDA) are useful to identify outliers, clusters, skewed distributions and correlations (see Figure 1 for examples implemented in our GeoDa software). Researchers routinely use these methods to find insights.However, what motivated this project is that, by default, it is easier to find insights that confirm the expected. Often, in fields like geography, statistics or computer science, available software and data drive the choice of research questions and the process of how we explore data. Typical insights gained this way are descriptive, like where a cluster is located or whether variables are correlated.To be sure, expected insights are reassuring. And descriptive insights are important. But instead of stopping here we want to build on them to also find insights that are new and relevant – we want to discover the unexpected (Anselin, 1998; Kielman & May, 2009). And, while we do need to know where clusters and outliers are – as researchers, we also want to go further and explain why these patterns exist (Good, 1983). In this project we presume that there are ways for structuring the process of data exploration that make it more likely to discover unexpected and explanatory insights (Platt, 1964).This presentation summarizes results from a summer 2020 lab where we started experimenting with how to do this using our Center’s GeoDa software. The summer lab was directed by Julia Koschinsky of the University of Chicago’s Center for Spatial Data Science. Marcos Falcone helped mentor five young University of Chicago and high school students for 7–10 weeks (majoring in statistics, computation, geography, political science, and economics).Our approach was to draw on philosophy of science and scientific reasoning to understand how the discovery of unexpected and explicable insights can work. We then tried to translate this to research designs for ESDA. Finally, we implemented the designs in replicable prototype examples for teaching and learning spatial research at the undergraduate or high school level.For instance, in terms of scientific reasoning, classic work on causal explanations (Mill, 1843) augments the typical current focus on correlations by also highlighting the need to assess the plausibility of your own explanation versus alternatives. This requires a mindset and practice of rigorously testing how our explanations might be wrong (Popper, 1959) rather than confirming that they're right (Nuzzo, 2015; Kahneman, 2011). To do this requires an iterative exchange between data and explanations – referred to as abductive reasoning, as it combines inductive and deductive approaches (Peirce, 1878; Heckman and Singer, 2017). We used Sherlock Holmes stories and the famous John Snow cholera case to illustrate the structure of these scientific reasoning concepts for a high school context (Coleman, 2019; cf Konnikova, 2013; Vinten-Johansen, 2020).Scientific reasoning goes back hundreds of years. Our challenge this summer and from here on has been to translate this reasoning to research designs that are applicable to modern interactive ESDA tools. Each of us developed four prototype resources for teaching and learning ESDA in GeoDa that we will develop further (Fig. 2): 1) protocols for how this could be done; 2) case examples to apply and revise the protocol; 3) GeoDa demo scripts to make the examples replicable; and 4) cleaned data and documentation. These resources will be released as part of a GeoDa Cookbook in the near future.Fig. 3 illustrates one of the protocols that differs from how ESDA is typically navigated. The starting point is the exploration of patterns in the outcome variable of interest. Next is the formulation of alternative explanations whose patterns plausibly match those of the outcome variable. Then we draw on quasi-experimental research designs to structure the testing of this match (Shadish et al., 2002). Finally, data about the hypothesized explanations are analyzed with ESDA and regressions to test or reformulate the hypotheses as part of an abductive process.

As walkable neighborhoods have grown in popularity, the supply of these neighborhoods has not kept up with the demand. However, the supply could increase through the use of planning tools related to zoning, land use, and urban form. While it seems as though single-family and low-density development would work against walkable neighborhoods, relatively few studies have examined this empirically. Thus, this paper seeks to address this research gap by applying descriptive, factor, cluster, and spatial regression analysis to data from six U.S. cities: Atlanta, Boston, Chicago, Miami, Phoenix, and Seattle. As expected, the following factors turned out to be significantly associated with walkable access to amenities: flexible, mixed use, commercial (including downtown, neighborhood scale, and sometimes big box), multifamily residential, and pedestrian-friendly residential (e.g., higher density, sidewalks, street trees, narrower streets, lower speed limits). Also as expected, single-family residential zoning (especially when isolated from other zoning categories) and low-density building characteristics were inversely related to walkable access. The paper concludes with an argument that the zoning types, land uses, and urban-form characteristics that were significantly associated with walkability should be extended to address the undersupply of walkable neighborhoods.

We investigated changes in supermarket access in Chicago between 2007 and 2014, spanning The Great Recession, which we hypothesized worsened local food inequity. We mapped the average street network distance to the nearest supermarket across census tracts in 2007, 2011, and 2014, and identified spatial clusters of persistently low, high or changing access over time. Although the total number of supermarkets increased city-wide, extremely low food access areas in segregated, low income regions did not benefit. Among black and socioeconomically disadvantaged residents of Chicago, access to healthy food is persistently poor and worsened in some areas following recent economic shocks.

The Medicare Modernization Act of 2003, implemented in 2006, increased managed care options for seniors. It introduced insurance plans for prescription drug coverage for all Medicare beneficiaries, whether they were enrolled in FFS or managed care (Medicare Advantage) plans. The availability of drug coverage beginning in 2006 served to free up budgets for FFS Medicare enrollees that could be used to make copayments for colorectal cancer (CRC) screening using endoscopy (colonoscopy or sigmoidoscopy). In 2007, Medicare eliminated the copayments required by seniors for CRC screening by endoscopy. Later in 2008, CRC screening by colonoscopy became part of the gold standard for CRC screening. This legitimized its use and offered even further encouragement to seniors, who may have been reluctant to undergo the procedure because of the non-pecuniary risks associated with it. In addition, 37 CRC screening interventions occurred during this timeframe to enhance compliance with screening standards. Using multilevel analysis of individuals’ endoscopy utilization, derived from 100% FFS Medicare claims, along with county-level market and contextual factors, we compare the periods before and after the MMA (2001–2005 to 2006–2009) to determine whether disparities in the utilization of endoscopic CRC screening occurred or changed over the decade. We examined Blacks, Asians, and Hispanics relative to Whites, and Females relative to Males (with race or ethnicity combined). We examined each state separately for evidence of disparities within states, to avoid confounding by geographic disparities. We expected that the net effect of the policy changes and the targeted interventions over the decade would be to increase CRC screening by endoscopy, reducing disparities. We saw improvements over time (reduced disparities relative to Whites) for Blacks and Hispanics residing in several states, and improvements over time for Females relative to Males in many states. For the vast majority of states, however, disparities persisted with Whites and Males exhibiting greater rates of utilization than other groups. States that undertook the interventions were more likely to have had improvements in disparities or positive disparities for women and minorities. While some gains were made over this time period, the gains were unevenly distributed across the USA and more work needs to be done to reduce remaining disparities.

Longitudinal analysis of supermarkets over time is essential to understanding the dynamics of foodscape environments for healthy living. Supermarkets for 2007, 2011, and 2014 for the City of Chicago were curated and further validated. The average distance to all supermarkets along the street network was constructed for each resident-populated census tract. These analytic results were generated with GIS software and stored as spatially enabled data files, facilitating further research and analysis. The data presented in this article are related to the research article entitled “Urban foodscape trends: Disparities in healthy food access in Chicago, 2007–2014” (Kolak et al., 2018).

The goal of this study is to quantitatively examine the relationship between walkability and arts-related businesses in metropolitan areas across the United States. Model results indicate that the relationship between arts businesses and walkability is sensitive to the size or scale of the business considered, as well as to the definition of the arts used. Larger-scale businesses are somewhat more likely to locate in walkable neighborhoods than are small-scale arts-related businesses, which are less likely to locate in walkable neighborhoods. This difference is likely due to the higher cost of property in these neighborhoods. In this regard, community-level economic development and planning entities need to take proactive measures to ameliorate the cost externalities associated with modifications to urban environments to make them more walkable.

As walkable neighborhoods are rapidly gaining popularity, these location-efficient areas are becoming less affordable to low-income tenants. We ask to what extent project- and tenant-based federal housing assistance is keeping these areas affordable and whether tradeoffs exist. Using descriptive statistical and logistic regression analysis for a data set of 3.8 million U.S. Department of Housing and Urban Development (HUD) tenants and a variety of neighborhood-level indicators, we find that HUD assistance provides tenants with differential access to walkable neighborhoods. Tenants who are senior, Asian, White, or have disabilities have higher chances of living in higher opportunity walkable areas. However, for those tenants with the greatest disadvantages (African American and Hispanic tenants), neighborhood quality remains compromised by higher poverty, segregation, and worse school quality, even in walkable neighborhoods. We identify the type of assistance (public housing, project-based rental assistance, and Housing Choice Vouchers) that is associated with compromised or higher opportunity access for these groups. This information can help prioritize assisted housing counseling, preservation, and siting to reduce existing spatial inequalities related to walkable amenity access, especially for African American and Hispanic tenants. This research also helps advance emerging research on the conceptualization and measurement of neighborhoods that integrates urban form and socioeconomic indicators.

The question of how to transform suburbs into sustainable neighborhoods lies at the forefront of current planning research. However; many infill and redevelopment efforts remain piecemeal. This article argues that systemic densification can increase the benefits of retrofitting. In particular, the authors propose smart densification processes designed to transform collector roads into pedestrian-friendly streets and watercourses into local parks. Such a densification approach can attract growth and enhance pedestrian access to transportation, daily services, and neighborhood facilities. The proposal is illustrated with an example from the Metropolitan Region of Barcelona (Catalonia, Spain). The authors suggest that only a holistic understanding of suburbs will allow planners to implement successful strategies for systemic densification that increase residents' quality of life.

Social science research, public and private sector decisions, and allocations of federal resources often rely on data from the American Community Survey (ACS). However, this critical data source has high uncertainty in some of its most frequently used estimates. Using 2006–2010 ACS median household income estimates at the census tract scale as a test case, we explore spatial and nonspatial patterns in ACS estimate quality. We find that spatial patterns of uncertainty in the northern United States differ from those in the southern United States, and they are also different in suburbs than in urban cores. In both cases, uncertainty is lower in the former than the latter. In addition, uncertainty is higher in areas with lower incomes. We use a series of multivariate spatial regression models to describe the patterns of association between uncertainty in estimates and economic, demographic, and geographic factors, controlling for the number of responses. We find that these demographic and geographic patterns in estimate quality persist even after we account for the number of responses. Our results indicate that data quality varies across places, making cross-sectional analysis both within and across regions less reliable. Finally, we present advice for data users and potential solutions to the challenges identified.

This article tests the extent to which a measure of walkable access is a good proxy for the quality of the walking environment. Based on existing findings on inequalities of walkability, we ask whether this relation varies between neighborhoods with low and high incomes. Walk Score is used to measure walkable access while the State of Place Index is applied to synthesize the qualitative urban form dimensions collected as part of the Irvine Minnesota Inventory. Simple bivariate correlations and difference-in-means tests assess the relationship and difference in average scores between the two. We draw on an existing sample of 115 walkable neighborhoods in the Washington, DC metro area that Mariela Alfonzo and colleagues had collected for previous research and that we augmented to include additional low-income neighborhoods. Our results reveal a strong and positive overall association between walkable access (Walk Score) and walkability (State of Place). However, this association masks problems with the quality of the walking environment that are significantly larger in low-income neighborhoods (even those with very good walkable access), especially regarding connectivity, personal safety, and the presence of litter and graffiti. As a proxy for walkability, Walk Score’s walkable access measure is, therefore, not equally strong across all neighborhoods but declines with income. In this sense, Walker’s Paradise is more walkable in higher than low-income neighborhoods.

In this paper, we investigate the disconnect between a parcel's actual land use and its corresponding zoning designation, focusing in particular on how single-family residential parcels are zoned. Using a unique set of detailed parcel information, we quantify the extent to which single-family land use is zoned as multi-family in the city of Phoenix, AZ. We carry out local spatial autocorrelation analysis, spatial regression, and regression models for proportions to analyze the pattern and associated explanatory factors for the fraction of single-family land use acreage by census tract that was zoned as multi-family. We find that the basic driver of mis-matched parcels at the tract scale is socio-economic, not physical or planning goal oriented. (C) 2016 Elsevier B.V. All rights reserved.