Sergio Rey

University of California, Riverside | UCR · Center for Geospatial Sciences

Cognitive health interventions often highlight individual or environmental factors, with research perspectives often siloed between psychological and demography disciplines. With key interest in understanding the rural-urban health divide, we examined how behavioral engagement and geospatial features influence cognitive functioning in individuals a...

This paper investigates the challenges and opportunities arising from incommensurate spatial partitions (ISPs) in regional science and spatial econometrics, focusing on how processes with overlapping yet distinct boundaries, interact and influence each other. ISPs are prevalent in various domains, including housing markets, employment centers, voti...

This research addresses the critical need for a comprehensive understanding of the spatial dynamics between public schools and residential neighborhoods, both of which significantly influence children’s life outcomes. The study introduces innovative methodologies for examining the spatial relationships between school catchment areas and neighborhoo...

Understanding neighborhood context is critical for social science research, public policy analysis, and urban planning. The social meaning, formal definition, and formal operationalization of “neighborhood” depends on the study or application, however, so neighborhood analysis and modeling requires both flexibility and adherence to a formal pipelin...

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...

This paper investigates the challenges and opportunities arising from incommensurate spatial partitions (ISPs) in regional science and spatial econometrics, focusing on how processes with overlapping yet distinct boundaries, interact and influence each other. ISPs are prevalent in various domains, including housing markets, employment centers, voti...

Discrete state Markov chains are widely used in regional science and economics to model spatial dynamics of a host of processes. This paper introduces an approach that extends the classic univariate focus to consider the joint spatial dynamics of two variables based on a Kronecker product framework. A test for interactions between the processes is...

Racial residential segregation is a longstanding topic of focus across the disciplines of urban social science. Classically, segregation indices are calculated based on areal groupings (e.g., counties or census tracts), with more recent research exploring ways that spatial relationships can enter the equation. Spatial segregation measures embody th...

American Community Survey (ACS) data have become the workhorse for the empirical analysis of segregation in the U.S.A. during the past decade. The increased frequency the ACS offers over the 10-year Census, which is the main reason for its popularity, comes with an increased level of uncertainty in the published estimates due to the reduced samplin...

Racial segregation in public education has been declared as unconstitutional for over 60 years in the United States. Yet many public school districts remain largely separate and unequal. A commonly used approach to reduce school segregation is redelineating school attendance zones to create more racially diverse classrooms. However, there is a need...

Wildland–urban interfaces (WUIs), the juxtaposition of highly and minimally developed lands, are an increasingly prominent feature on Earth. WUIs are hotspots of environmental and ecological change that are often priority areas for planning and management. A better understanding of WUI dynamics and their role in the coupling between cities and surr...

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...

In this paper we move away from a static view of neighbourhood inequality and investigate the dynamics of neighbourhood economic status, which ties together spatial income inequality at different moments in time. Using census data from three decades (1980–2010) in 294 metropolitan statistical areas, we use a statistical decomposition method to unpa...

A recent review noted important differences in the results of the local Moran's statistic depending on the inference method. These differences had significant practical implications. In closing, the authors speculated the differences may be due to local spatial heterogeneity. In this article, we propose that different null hypotheses, not heteroske...

To build educational capacity for the rapidly evolving science and profession of geocomputation, the American Association of Geographers piloted an Encoding Geography research-practice partnership (RPP) composed of geography and computer science educators and researchers. This commentary describes the process, known as Collective Impact, that was i...

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...

Bivand and Wong (2018), a recent review on spatial statistical software, noted important differences in the results of the local Moran’s Ii statistic depending on the method of inference. That review speculated the differences may be due to the presence of local spatial heterogeneity. In this paper we design an experiment to assess the impact of lo...

We report microplastic densities on windward beaches of Oahu, Hawai`i, USA, an island that received about 6 million tourist visits a year. Microplastic densities, surveyed on six Oahu beaches, were highest on the beaches with the coarsest sands, associated with high wave energy. On those beaches, densities were very high (700–1700 particles m⁻²), a...

Comparative segregation analysis holds the potential to provide rich insights into urban socio-spatial dynamics. However, comparisons of the levels of segregation between two, or more, cities at the same point in time can be complicated by different spatial contexts as well as ethnic, racial, and class distributions. The extent to which differences...

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...

Developing spatial analytical methods as open source libraries is an important endeavor to enable open and replicable science. However, despite the fact that large geospatial data and geospatial cyberinfrastructure (GeoCI) resources are becoming available, many libraries and toolkits are only initialized and designed for analytics in a desktop envi...

Regional income convergence and divergence has been an active field of research for more than 20 years, and research papers in this field are still being produced at a prodigious rate. Despite their importance for the study of dynamics of income distribution, interactive visualization tools revealing spatiotemporal dimensions of the income data hav...

Neighborhood delineation is increasingly relied upon in urban social science research to identify the most appropriate spatial unit. In problems of this type, the true number of neighborhoods (typically called the k parameter) is unknown and analysts often require algorithmic approaches to determine k endogenously. Existing approaches for neighborh...

There is a recent surge in research focused on urban transformations in the United States via empirical analysis of neighborhood sequences. The alignment-based sequence analysis methods have seen many applications in urban neighborhood change research. However, it is unclear to what extent these methods are robust in terms of producing consistent a...

In human geography and the urban social sciences, the segregation literature typically engages with five conceptual dimensions along which a given society may be considered segregated: evenness, isolation, clustering, concentration and centralization (all of which can incorporate or omit spatial context). Over the last several decades, dozens of se...

Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective exploration of multiple...

Rurality is associated with cognitive health disparities. We investigated proximal and distal indices of rurality, activity engagement and cognitive performance in the ongoing Colorado Adoption/Twin Study of Lifespan behavioral development and cognitive aging (CATSLife; N = 979; 47% female). The Index of Relative Rurality (IRR) (0 = Urban to 1= Rur...

Open science practices are a large and healthy part of computational geography and the burgeoning field of spatial data science. In many forms, open geospatial cyberinfrastructure adheres to a varying and informal set of practices and codes that empower levels of collaboration that are impossible otherwise. Pathbreaking work in geographical science...

Empirical applications of the Markov chain model and its spatial extensions suffer from issues induced by the sparse transition probability matrix, which usually results from adopting maximum likelihood estimators (MLEs). Two discrete kernel estimators with cross‐validated parameters are proposed for reducing the sparsity in the estimated transitio...

Regionalization, under various guises and descriptions, is a longstanding and pervasive interest of urban studies. With an increasingly large number of studies on urban place detection in language, behavior, pricing, and demography, recent critiques of longstanding regional science perspectives on place detection have focused on the arbitrariness a...

This paper considers the past and future of the journal Geographical Analysis (GA), as well as the broader field of spatial analysis. From my experiences as a former editor of GA, I first identify three external trends that I feel will provide the backdrop for the future evolution of spatial analysis. These surround the rise of artificial intellige...

This paper examines the field of regional science from the perspective of wider developments surrounding open-source software and the rising open-science movement. Regional science has been fairly isolated from these currents and a number of possible explanations for that isolation are identified. Opportunities that the emerging fields of data scie...

Understanding human movements in the face of natural disasters is critical for disaster evacuation planning, management, and relief. Despite the clear need for such work, these studies are rare in the literature due to the lack of available data measuring spatiotemporal mobility patterns during actual disasters. This study explores the spatiotempor...

For close to a century, researchers from across the disciplines of Urban Studies have developed empirical models for understanding the spatial extent and social composition of urban neighborhoods--and how these dimensions change over time. Unfortunately, however, these techniques have often been developed within disciplinary silos and without broad...

Regionalization, under various guises and descriptions, is a longstanding and pervasive interest of urban studies. With an increasingly large number of studies on urban place detection in language, behavior, pricing, and demography, recent critiques of longstanding regional science perspectives on place detection have focused on the arbitrariness a...

Income mobility measures provide convenient and concise ways to reveal the dynamic nature of regional income distributions. Statistical inference about these measures is important especially when it comes to a comparison of two regional income systems. Although the analytical sampling distributions of relevant estimators and test statistics have be...

Monitoring market share changes over space and time is an essential and continuous task for commercial companies and their third-party local agents to adjust their sale campaigns and marketing efforts for profit maximisation. This paper uses social media data as a cheap and up-to-date source to reveal the implicit semantics that are embedded in the...

Stochastic dominance tests are used to measure whether distributions are directionally‐distinct. Using stochastic dominance measures, an income distribution can be measured to be more favorable for its members at all income levels than another distribution. This contrasts with conventional σ and β‐convergence approaches that compare distributions a...

Spatial effects have been recognized to play an important role in transitional dynamics of regional incomes. Detection and evaluation of both spatial heterogeneity and spatial dependence in discrete Markov chain models, which have been widely applied to the study of regional income distribution dynamics and convergence, are vital, but under-explore...

This chapter examines the potential opportunities that open source offers for research and education in spatial analysis. Drawing on lessons learned in the development of PySAL: Python Library for Spatial Analysis, it touches on the opportunities and challenges related to the adoption of open source practices and culture. While open source has had...

The United Nations expressed an interest in reducing subnational (i.e., provinces, states) inequality. We propose using a spatial decomposition of the Gini coefficient (SDGC) to track changes in subnational inequality. Typically, agencies do not track summary measures of subnational clustering of development indicators. Tracking changes in the SDGC...

Urbanization is a natural and social process involving simultaneous changes to the Earth’s land systems, energy flow, demographics, and the economy. Understanding the spatiotemporal pattern of urbanization is increasingly important for policy formulation, decision making, and natural resource management. A combination of satellite remote sensing an...

This paper explores the stability of local vehicle ownership levels in Great Britain using the technique of spatial Markov analysis. Non-spatial Markov processes describe the transition of areas through levels of ownership with no regard to their neighbourhood context. This approach is likely to be lacking in plausibility. For example, an area loca...

This chapter (This manuscript is a chapter version of the original document, which is a reproducible online notebook. The entire, version-controlled project can be found online at: https://bitbucket.org/darribas/reproducible_john_snow.) presents an entirely reproducible spatial analysis of the classic John Snow’s map of the 1854 cholera epidemic in...

Because so many present and future challenges are regional in scope, regional solutions will be needed, and the next 50 years of research at the regional level will only grow in importance. In the future, appropriate regional scales will be supranational, national, and subnational, many global problems will require localized solutions, and there wi...

This paper examines how “lumping”, the aggregation of different states of a Markov chain into one state, affects the underlying properties of the Markov process. Specifically, a Markov chain model of income convergence for US states is estimated, and different quantile lumpings are tested to determine if they preserve the Markov property. This work...

Discrete Markov chain models (DMCs) have been widely applied to the study of regional income distribution dynamics and convergence. This popularity reflects the rich body of DMC theory on the one hand and the ability of this framework to provide insights on the internal and external properties of regional income distribution dynamics on the other....

Within residential electricity consumption there exists significant variability from home-to-home due to the differences in building thermal properties, appliances, and inhabitants. Electricity analyses at sub-city scales using predefined geographies, such census tracts, might artificially split areas with homogenous characteristics leading to anal...

Large data contexts present a number of challenges to optimal choropleth map classifiers. Application of optimal classifiers to a sample of the attribute space is one proposed solution. The properties of alternative sampling-based classification methods are examined through a series of Monte Carlo simulations. The impacts of spatial autocorrelation...

What factors are related to establishment dynamics following disturbance in late-successional versus second-growth tropical forests of the Pacific islands? Are those relationships robust to interannual fluctuations in establishment? In three sites juveniles were enumerated in 30 (5 × 5-m) subplots within 45 × 50-m tree plots in 2004 and 2005, 2.5 a...

Understanding the impacts of land cover pattern on the heat island effect is essential for sustainable urban development. Conventional model fitting methods have restricted ability to produce accurate estimates of the land cover-temperature association due to the lack of procedures to address two important issues: spatial dependence in proximal spa...

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...

In the study of income inequality dynamics, the concept of exchange mobility plays a central role. Applications of classical rank correlation statistics have been used to assess the degree to which individual economies swap positions in the income distribution over time. These classic measures ignore the underlying geographical pattern of rank chan...

The best obstacle avoiding path in continuous space, referred to as the Euclidean shortest path, is important for spatial analysis, location modeling and wayfinding tasks. This problem has received much attention in the literature given its practical application, and several solution techniques have been proposed. However, existing approaches are l...

Social and interregional inequality patterns across US states from 1929–2012 are analyzed using exploratory space–time methods. The results suggest complex spatial dynamics for both inequality series that were not captured by the stylized model of Alonso. Interpersonal income inequalities of states displayed a U-shaped pattern ending the period at...

Discrete Markov chain theory has played a major role in the literature on regional income distributional dynamics. This paper examines a selection of methodological issues and choices related to the implementation of this framework, focusing on: [1] specification of class-state boundaries; [2] use of population weighted or unweighted transitions;...

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...

In this paper we examine the trajectory of regional income inequality dynamics for two neighboring national systems. Using data on 3038 US counties and 2418 Mexico municipios, from 2000, 2005, and 2010, we employ recent extensions of spatial Markov chains and space-time mobility measures, to consider the following questions: Are regional inequality...

Segregated areas may occur around an attractive park or a waste incinerator, but the magnitude and group membership of the people in closest proximity will likely be difierent. We therefore introduce a local segregation measure that can be applied to any location within a metropolitan area, and that can identify the group that is relatively more co...

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...

This chapter provides an overview of spatial dynamics in the field of regional science. After defining the context of spatial dynamics and the alternative conceptualizations of space and time, the chapter surveys the various areas of substantive interest where spatial dynamics come to the fore. A second focus is on the methodological and technical...

Peer reviewing is one of the core processes of science. While the typical blind system helps to improve original submissions, there are opportunities for academic publishing to learn from open source practices (commits, bug reports, feature requests, documentation, etc.), which are entirely open and done in public view. But beyond, with greater sig...

This presidential address will contrast two worlds of science. The first, and the one we regional scientists currently find ourselves embedded within, is what I will call captured science. While this is our status quo, it is not generally what holds everywhere in the broader scientific community where a second and new type of science is operative....

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....

Markov chains have become a mainstay in the literature on regional income distribution dynamics and convergence. Despite its growing popularity, the Markov framework has some restrictive characteristics associated with the underlying discretization income distributions. This paper introduces several new approaches designed to mitigate some of the i...

The industrial composition of places has received considerable attention because of the widespread belief that industrial diversity buffers regional economies from economic shocks. Subsequently, a variety of toolkits and indices have been developed with the goal of better capturing the compositional dynamics of regions. Although useful, a key drawb...