Luc Anselin

University of Chicago | UC · Center for Spatial Data Science

Access to treatment and medication for opioid use disorder (MOUD) is essential in reducing opioid use and associated behavioral risks, such as syringe sharing among persons who inject drugs (PWID). Syringe sharing among PWID carries high risk of transmission of serious infections such as hepatitis C and HIV. MOUD resources, such as methadone provid...

The passing of Professor Arthur Getis in May of 2022 initiated a number of events to both reflect on and remember the tremendous contributions he has made over his career to geographical systems more broadly, but also spatial analysis, spatial statistics, regional science, geography and GIScience, among others. This began with a series of sessions...

The search for the most appropriate spatial econometric specification has received considerable attention in the literature, where a major tension exists between a specific to general approach (STGE), mostly based on Rao Score/Lagrange Multiplier statistics, and a general to specific approach (GETS), based on Wald or Likelihood ratio statistics. Th...

The pioneering work of Getis and Ord on local spatial statistics has a counterpart in spatial econometrics in treating spatial heterogeneity. This can be approached from a continuous or a discrete perspective. In a discrete perspective, referred to as spatial regimes, the coefficients vary by discrete subregions of the data. Whereas the estimation...

Background: Access to treatment and medication for opioid use disorder (MOUD), such as methadone, is essential for improving health outcomes by reducing infection and overdose risks associated with injection drug use. MOUD resource distribution, however, is often a complex interplay of social and structural factors that result in nuanced patterns r...

John Snow’s quest to discover how cholera was transmitted during the mid-nineteenth century in London has become a classic case for teaching spatial data analysis, causal inference, scientific reasoning, quasi-experimental research design, and spatial epidemiology. In this PDF, our Center for Spatial Data Science at the University of Chicago is mak...

In the years between the Great Recession and the COVID-19 pandemic, large numbers of retail store closures prompted concern over a growing retail “apocalypse.” However, academic research on the spatial and temporal extent of such an apocalypse – and its relationship to regional economic vitality – is lacking. This chapter uses an extensive national...

Using mathematical demography to analyze the life and death of retail businesses in the US from 1990-2015, we found that the retail sector is becoming more corporate despite small and mid-size establishments retaining the largest share (~70%) of existing retail businesses. We also found the retail sector to be highly volatile, especially corporate...

Much progress has been made in the development of software tools for spatial analysis since the special issue of Geographical Analysis appeared in 2006, devoted to “Recent advances in software for spatial analysis in the social sciences” (Rey and Anselin 2006). The 15 some years since the publication of the issue have been marked by major changes i...

Despite growing awareness of opioid use disorder (OUD), fatal overdoses and downstream health conditions (e.g., hepatitis C and HIV) continue to rise in some populations. Various interrelated structural forces, together with social and economic determinants, contribute to this ongoing crisis; among these, access to medications for opioid use disord...

Cooperative banks primarily compete with one another because they target niche markets that large banks typically ignore. The current study shows that in this competitive environment, the connection between financial intermediaries affects the operational efficiency of small banks. The findings indicate that the capitalization, diversification stra...

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...

Lack of economic integration at the neighborhood scale is viewed as a significant problem given its association with aggregated advantage (concentrated wealth) or disadvantage (concentrated poverty). Scholarship on neighborhood-level social make-up has most often focused on the problem of racial and economic segregation, but we believe there are im...

GeoDa is an open source and cross-platform desktop software program for the visualization and exploration of geospatial data. GeoDa is not a geographic information system (GIS), but it incorporates many basic functions of a GIS, such as mapping, creating point layers from coordinates, and handling cartographic projections. The early versions of Geo...

In spatial econometrics, the treatment of spatial heterogeneity can be approached from a continuous or a discrete perspective. In a continuous approach, represented by methods such as geographically weighted regression (GWR) and Bayesian varying coefficient specifications, the model coefficients are allowed to vary smoothly over space. In contrast,...

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 thre...

PySAL is a library for geocomputation and spatial data science. Written in Python, the library has a long history of supporting novel scholarship and broadening methodological impacts far afield of academic work. Recently, many new techniques, methods of analyses, and development modes have been implemented, making the library much larger and more...

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 paper reports on the evolution in the functionality and architecture of th...

Previous studies have been focused primarily on modelling and predicting the transmission of COVID-19. While little research has been conducted to understand the impacts of different travel modes on the transmission of COVID-19, without an explicit understanding of the travel mode effects, many people intuitively perceive non-motorized travel modes...

Multilevel models have been applied to study many geographical processes in epidemiology, economics, political science, sociology, urban analytics, and transportation. They are most often used to express how the effect of a treatment or intervention might vary by geographical group, a form of spatial process heterogeneity. In addition, these models...

Where is public money for place-based improvement spent? A common narrative is that public expenditure prioritizes business and development interests at the expense of disadvantaged areas that have the greatest need. This paper is a quantified look at this question—an analysis of spending patterns at the neighborhood level. The spatial analysis of...

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 thre...

Please note that a new version of this document is available here: https://tinyurl.com/jyudsenb

Please note that a new version of this document is available here: https://tinyurl.com/jyudsenb

African American (AA) populations experience persistent health disparities in the USA. Low representation in bio-specimen research precludes stratified analyses and creates challenges in studying health outcomes among AA populations. Previous studies examining determinants of bio-specimen research participation among minority participants have focu...

Tobler’s first law of geography is widely recognized as reflecting broad empirical realities in geography. Its key concepts of “near” and “related” are intuitive in a univariate setting. However, when moving to the joint consideration of spatial patterns among multiple variables, the combination of attribute similarity and geographical similarity t...

Spatial data science is a subset of data science that takes into account the special characteristics of spatial data in analytical methods and software tools. Its emergence was stimulated by the advent of big data, much of which is georeferenced (and time stamped). This required a rethinking of traditional spatial analytical and geocomputation meth...

Background: Cities are becoming increasingly important habitats for mosquito-borne infections. The pronounced heterogeneity of urban landscapes challenges our understanding of the spatio-temporal dynamics of these diseases, and of the influence of climate and socio-economic factors at different spatial scales. Here, we quantify this joint influence...

In this theoretical note, we propose the GProbit model as an alternative to gravity models to estimate grouped-data flows. This is a model based on the random utility theory, which is consistent with the principle of population behavior. Instead of migrant counts, the dependent variable of the GProbit model of flows consists of a number of observed...

Empirical work in regional science has seen a growing interest in causal inference, leveraging insights from econometrics, statistics, and related fields. This has resulted in several conceptual as well as empirical papers. However, the role of spatial effects, such as spatial dependence (SD) and spatial heterogeneity (SH), is less well understood...

This note introduces the concept of quantile local spatial autocorrelation as a special case of a local indicator of spatial association (LISA) for the situation where the variables of interest are binary. This provides additional insight into the spatial distribution of observations at the extremes of the distribution. The concept is illustrated w...

This paper operationalizes the idea of a local indicator of spatial association for the situation where the variables of interest are binary. This yields a conditional version of a local join count statistic. The statistic is extended to a bivariate and multivariate context, with an explicit treatment of co-location. The approach provides an altern...

Background Cities are becoming increasingly important habitats for mosquito-borne infections. The pronounced heterogeneity of urban landscapes challenges our understanding of the spatio-temporal dynamics of these diseases, and of the influence of climate and socio-economic factors at different spatial scales. Here, we quantify this joint influence...

This chapter introduces a CyberGIS solution that aims at resolving the big data challenges in the discovery, search, visualization and interoperability of geospatial data. We describe a service-oriented architecture to make heterogeneous geospatial resources easily sharable and interoperable. OGC standards for sharing vector data, raster data, sens...

This paper extends the application of the Local Geary c statistic to a multivariate context. The statistic is conceptualized as a weighted distance in multivariate attribute space between an observation and its geographical neighbors. Inference is based on a conditional permutation approach. The interpretation of significant univariate Local Geary...

Multilevel (or variance components) models have been applied in many areas of regional science, epidemiology and polimetrics. They are most often used to model treatment nonstationarity in policy regimes, a form of spatial process heterogeneity. Multilevel models with spatially-correlated components are increasingly used to model the presence of bo...

In this paper, we present a quantified, GIS-based analysis of the relationship between urban morphological patterns and racial, ethnic, and household characteristics. We want to understand how the built landscapes of American cities differ in sociological terms—for example, are some more prone to racial concentration or prevalence of particular fam...

Spatial econometric specifications pose unique computational challenges to Bayesian analysis, making it difficult to estimate models efficiently. In the literature, the main focus has been on extending Bayesian analysis to increasingly complex spatial models. The stochastic efficiency of commonly used Markov Chain Monte Carlo (MCMC) samplers has re...

Advances in GIS are increasingly focused on providing more sophisticated spatial analytical capabilities. Much of this work assumes no attribute and positional uncertainties in data. While there has been considerable research devoted to enhanced data creation techniques and metadata associated with error and uncertainty, little has been done to cha...

Spatial SUR

Cloud mapping platforms, such as Google Maps, CartoDB, MapZen, and the like have become powerful alternatives to traditional desktop-based mapping and GIS applications. However, unlike their desktop counterparts, web mapping platforms typically do not contain any but the most basic spatial analytical functionality. In this paper, we introduce GeoDa...

With the advent of ‘big data’ there is an increased interest in using social media to describe city dynamics. This paper employs geo-located social media data to identify ‘digital neighborhoods’ – those areas in the city where social media is used more often. Starting with geo-located Twitter and Foursquare data for the New York City region in 2014...

Over the last two decades information technology (IT) outsourcing has grown dramatically, and has emerged as a strategic choice for firms searching for ways to control their costs and maintain a competitive edge. The mechanisms driving its growth are not fully understood though. In this research, we employ an approach that focuses on geographic, te...

This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical user interface, command line or embedded in a computational notebook), open spatial analytics middleware, and web, cloud and distributed open geospa...

In this paper, we report efforts to develop a parallel implementation of the p-compact regionalization problem suitable for multi-core desktop and high-performance computing environments. Regionalization for data aggregation is a key component of many spatial analytical workflows that are known to be NP-Hard. We utilize a low communication cost par...

Geospatial Semantic Web promises better retrieval geospatial information for Digital Earth systems by explicitly representing the semantics of data through ontologies. It also promotes sharing and reuse of geospatial data by encoding it in Semantic Web languages, such as RDF, to form geospatial knowledge base. For many applications, rapid retrieval...

Spatial analysis of Big data is a key component of Cyber-GIS. However, how to utilize existing cyberinfrastructure (e.g. large computing clusters) to perform parallel and dis-tributed spatial analysis on Big data remains a huge chal-lenge. Problems such as ecient spatial weights creation, spatial statistics and spatial regression of Big data still...

Rapid retrieval of spatial information is critical to ensure that emergency supplies and resources can reach the impacted areas in the most efficient manner. However, it remains challenging to find out the needed spatial information efficiently because of the intensive geocomputation processes involved and the heterogeneity of spatial data. It is q...

Within a CyberGIS environment, the development of effective mechanisms to encode metadata for spatial analytical methods and to track the provenance of operations is a key requirement. Spatial weights are a fundamental element in a wide range of spatial analysis methods that deal with testing for and estimating models with spatial autocorrelation....

This article introduces a new approach to the analysis of changes in sex offender residences over time. Using a Markov chain framework, we analyze residential movement patterns of registered sex offenders in Hamilton County, Ohio, over a three-year period (2005–2007). Results indicate a 46 percent reduction in offenders violating spatial restrictio...

CyberGIS – defined as cyberinfrastructure-based geographic information systems GIS – has emerged as a new generation of GIS representing an important research direction for both cyberinfrastructure and geographic information science. This study introduces a 5-year effort funded by the US National Science Foundation to advance the science and applic...

This book aims to present a profile of the research done around the world in the field of commercial geography and to locate in this context the research done in Mexico, particularly that which is done in El Colegio Mexiquense, one of the research centers that has explored these issues with particular interest in Mexican cities. El Colegio Mexiquen...

In this paper, we investigate the finite sample properties of Moran’s I test statistic for spatial autocorrelation in limited dependent variable models suggested by Kelejian and Prucha (2001). We analyze the socio- economic determinants of the availability of dialysis equipment in 5,507 Brazilian municipalities in 2009 by means of a probit and to...

Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes....

In this article, we report on our experiences with refactoring a spatial analysis library to support parallelization. Python Spatial Analysis Library PySAL is a library of spatial analytical functions written in the open-source language, Python. As part of a larger scale effort toward developing cyberinfrastructure of GIScience, we examine the part...

We investigate the common conjecture in applied econometric work that the inclusion of spatial fixed effects in a regression specification for a single cross-sectional data set removes spatial dependence. We demonstrate analytically and by means of a series of simulation experiments how evidence of the removal of spatial autocorrelation by spatial...

The purpose of this guide is to provide an overview of how to use the main functionality of the Crime Analytics for Space-Time program (CAST), which was developed by Arizona State University’s GeoDa Center for Geospatial Analysis and Computation under a 2009- 12 cooperative agreement with the National Institute of Justice.1 Since we have made detai...

Andy Isserman left a lasting impact on the fields of Regional Science and Planning. This special issue is dedicated to his memory. In addition to summarizing its contents, in this article I also reflect on Andy’s particular view of Regional Science as a field, expressed in many of his writings as well as countless personal conversations.

The social, economic, and cultural impacts of sex offender legislation are topics of considerable interest in recent years. Despite the number of studies evaluating the collateral consequences of these laws, the implications of spatial restrictions on housing availability and residential mobility for convicted sex offenders remain an empirical ques...

In this article, we focus on the evolution of the technology that lies at the basis of implementing spatial econometric methods into software tools. We review the changing methodological emphases and their implications for data structures and computational infrastructure required for estimation and inference. We review the evolution of software sol...

Hedonic house price models are increasingly applied in the process of mass appraisal, in which econometric specifications are used to obtain automated valuation of properties for taxation purposes. The predictive quality of such models is important, since it directly affects the revenue stream of local authorities. In this paper, we assess the rela...

Change and movement across space and over time are observed in our everyday lives, with people commuting, traveling, communicating, moving, migrating, etc. Understanding how and why such change occurs is important for various reasons, including management of resources, planning for service improvements, detecting whether there are anomalies of some...

Each state is autonomous in its comprehensive cancer control (CCC) program, and considerable heterogeneity exists in the program plans, but researchers often focus on the concept of nationally representative data and pool observations across states using regression analysis to come up with average effects when interpreting results. Due to considera...

In this note, we compare three test statistics that have been suggested to assess the presence of spatial error autocorrelation in probit models. We highlight the formal differences between the tests proposed by Pinkse and Slade (1998), Pinkse (1999, 2004) and Kelejian and Prucha (2001), and compare their properties in a extensive set of Monte Carl...

Climate change is likely to result in increased aridity, lower runoff, and declining water supplies for the cities of the Southwestern United States, including Phoenix. The situation in Phoenix is particularly complicated by the large number of water providers, each with its own supply portfolio, demand conditions, and conservation strategies. This...

This essay assesses the evolution of the way in which spatial data analytical methods have been incorporated into software tools over the past two decades. It is part retrospective and prospective, going beyond a historical review to outline some ideas about important factors that drove the software development, such as methodological advances, the...

Patterns in loss‐ratio experience in the U.S. corn insurance market are investigated with a spatial econometric model. The results demonstrate systematic geographically related misratings and provide estimates of the impacts of several observable factors on the magnitude of misrating in the program. The model is used to estimate actuarial cross‐sub...

Patterns in loss‐ratio experience in the U.S. corn insurance market are investigated with a spatial econometric model. The results demonstrate systematic geographically related misratings and provide estimates of the impacts of several observable factors on the magnitude of misrating in the program. The model is used to estimate actuarial cross‐sub...

To determine whether Medicare managed care penetration impacted the diffusion of endoscopy services (sigmoidoscopy, colonoscopy) among the fee-for-service (FFS) Medicare population during 2001-2006. We model utilization rates for colonoscopy or sigmoidoscopy as impacted by both market supply and demand factors. We use spatial regression to perform...

This article assesses the performance of U.S. planning programs relative to their administrative location in design versus nondesign units. We use both archival data to compare program rankings between design and nondesign units and a survey of a random sample of faculty (108 at 61 accredited programs). The archival data show a higher publication p...

This article introduces a new approach to measuring neighborhood change. Instead of the traditional method of identifying “neighborhoods” a priori and then studying how resident attributes change over time, this approach looks at the neighborhood more intrinsically as a unit that has both a geographic footprint and a socioeconomic composition. Ther...

Despite the volume of literature afforded knowledge work and innovations in information and communications technologies (ICTs), few studies have examined the importance of ICTs to firms in knowledge industries. This study will develop spatial econometric models to examine the relative importance of broadband provision levels to knowledge intensive...

We investigate the common conjecture in applied econometric work that the inclusion of spatial fixed effects in a regression specification re- moves spatial dependence. We demonstrate analytically and by means of a series of simulation experiments how evidence of the removal of spatial autocorrelation by spatial fixed effects may be spurious when t...

1 The authors thank the anonymous JRS reviewers and Boris Dev for their in-sightful and helpful comments during the review process. The usual disclaimer applies.