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This paper investigates the long-term impacts of the federal Home Owners' Loan Corporation (HOLC) mortgage risk assessment maps on the spatial dynamics of recent income and racial distributions in California metropolitan areas over the 1990-2010 period. We combine historical HOLC boundaries with modern Census tract data and apply recently developed methods of spatial distribution dynamics to examine if legacy impacts are reflected in recent urban dynamics. Cities with HOLC assessments are found to have higher levels of isolation segregation than the non-HOLC group, but no difference in unevenness segregation between the two groups of cities are found. We find no difference in income or racial and ethnic distributional dynamics between the two groups of cities over the period. At the intra-urban scale, we find that the intersectionality of residing in a C or D graded tract that is also a low-income tract falls predominately upon the minority populations in these eight HOLC cities. Our findings indicate that neighborhoods with poor housing markets and high minority concentrations rarely experience a dramatic change in either their racial and ethnic or socioeconomic compositions—and that negative externalities (e.g. lower home prices and greater segregation levels) emanate from these neighborhoods, with inertia spilling over into nearby zones. [ABSTRACT FROM AUTHOR]

Big data, the "new oil" of the modern data science era, has attracted much attention in the GIScience community. However, we have ignored the role of code in enabling the big data revolution in this modern gold rush. Instead, what attention code has received has focused on computational efficiency and scalability issues. In contrast, we have missed the opportunities that the more transformative aspects of code afford as ways to organize our science. These "big code" practices hold the potential for addressing some ill effects of big data that have been rightly criticized, such as algorithmic bias, lack of representation, gatekeeping, and issues of power imbalances in our communities. In this article, I consider areas where lessons from the open source community can help us evolve a more inclusive, generative, and expansive GIScience. These concern best practices for codes of conduct, data pipelines and reproducibility, refactoring our attribution and reward systems, and a reinvention of our pedagogy. [ABSTRACT FROM AUTHOR]

In this paper we examine the evolution of urban spatial structure in U.S. metropolitan areas over nearly two decades. Using annual block-level data from the Longitudinal Employment Household Dynamics database, we introduce a technique for identifying regional employment centers that both adheres to urban economic theory and pays homage to classic contributions in local spatial statistics. Centers are defined as local spatial statistical outliers on the network-based job accessibility surface. We proceed by identifying the location and employment makeup of centers for each metropolitan region in the USA from 2002 to 2019 and discuss emergent trends across time and space. Critically, we not only explore empirical patterns, but we discuss the relationship between polycentricity, the evolution of urbanization and localization economies, and regional specialization. We confirm again the pattern of polycentricity in U.S. metros and show that the structure of metropolitan employment is largely stable over time. We also document a continuing trend away from urbanization economies into more specialized subcenters. • We develop a new technique for identifying employment subcenters combining spatial statistics, network analysis, and computational geometry. • the technique generates bespoke polygons that can be used for further analysis or designated as targets for urban development. • We apply our method to 20 years of annual census block -level employment from LEHD and discuss the trend toward increasing polycentric development in the U.S. • We document the increasing prominence of more specialized localization economies. • The methodology we contribute is generalizeable and can be applied anywhere in the world (we provide a Python software package for reproducing the results) [ABSTRACT FROM AUTHOR]

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 in the spatial analytical software landscape. Arguably, three important and somewhat related phenomena can be distinguished that drove these changes: the embedding of spatial analysis into spatial data science; the growing recognition of open science/open source principles in empirical work; and the increasing adoption of a literate programming perspective. [ABSTRACT FROM AUTHOR]

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 encompassing than that previously discussed in the literature. As such, we provide an introduction to the library as it stands now, as well as the scientific and conceptual underpinnings of its core set of components. Finally, we provide a prospective look at the library's future evolution. [ABSTRACT FROM AUTHOR]

Spatial data regularly suffer from error and uncertainty, ranging from poorly georeferenced coordinate pairs to sampling error associated with American Community Survey data. Geographic information systems can amplify and propagate error and uncertainty through the abstraction and representation of spatial data, as can the manipulation, processing, and analysis of spatial data using exploratory and confirmatory statistical techniques. The purpose of this article is to explore and address uncertainty in regionalization, a fundamental spatial analytical method that aggregates spatial units (e.g., tracts) into a set of contiguous regions for strategic purposes, including school districting, habitat areas, and the like. Specifically, we develop a new regionalization method, the uncertain‐max‐p‐regions problem that explicitly incorporates attribute uncertainty and allows its impacts to be evaluated with a degree of statistical certainty. We also detail an efficient solution approach for dealing the problem. The results suggest that the developed problem can out‐perform existing regionalization approaches and that the addition of a measure of statistical confidence can help to facilitate more clarity in planning and policy decisions. [ABSTRACT FROM AUTHOR]

The max‐p‐compact‐regions problem involves the aggregation of a set of small areas into an unknown maximum number (p) of compact, homogeneous, and spatially contiguous regions such that a regional attribute value is higher than a predefined threshold. The max‐p‐compact‐regions problem is an extension of the max‐p‐regions problem accounting for compactness. The max‐p‐regions model has been widely used to define study regions in many application cases since it allows users to specify criteria and then to identify a regionalization scheme. However, the max‐p‐regions model does not consider compactness even though compactness is usually a desirable goal in regionalization, implying ideal accessibility and apparent homogeneity. This article discusses how to integrate a compactness measure into the max‐p regionalization process by constructing a multiobjective optimization model that maximizes the number of regions while optimizing the compactness of identified regions. An efficient heuristic algorithm is developed to address the computational intensity of the max‐p‐compact‐regions problem so that it can be applied to large‐scale practical regionalization problems. This new algorithm will be implemented in the open‐source Python Spatial Analysis Library. One hypothetical and one practical application of the max‐p‐compact‐regions problem are introduced to demonstrate the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

geopyter, an acronym of Geographical Python Teaching Resources, provides a hub for the distribution of 'best practice' in computational and spatial analytic instruction, enabling instructors to quickly and flexibly remix contributed content to suit their needs and delivery framework and encouraging contributors from around the world to 'give back' whether in terms of how to teach individual concepts or deliver whole courses. As such, geopyter is positioned at the confluence of two powerful streams of thought in software and education: the free and open-source software movement in which contributors help to build better software, usually on an unpaid basis, in return for having access to better tools and the recognition of their peers); and the rise of Massive Open Online Courses, which seek to radically expand access to education by moving course content online and providing access to students anywhere in the world at little or no cost. This paper sets out in greater detail the origins and inspiration for geopyter, the design of the system and, through examples, the types of innovative workflows that it enables for teachers. We believe that tools like geopyter, which build on open teaching practices and promote the development of a shared understanding of what it is to be a computational geographer represent an opportunity to expand the impact of this second wave of innovation in instruction while reducing the demands placed on those actively teaching in this area. [ABSTRACT FROM AUTHOR]

AbstractModels of human mobility can be broadly applied to find solutions addressing diverse topics such as public health policy, transportation management, emergency management, and urban development. However, many mobility models require individual-level data that is limited in availability and accessibility. Synthetic populations are commonly used as the foundation for mobility models because they provide detailed individual-level data representing the different types and characteristics of people in a study area. Thorough evaluation of synthetic populations is required to detect data biases before the prejudices are transferred to subsequent applications. Although synthetic populations are commonly used for modeling mobility, they are conventionally validated by their sociodemographic characteristics, rather than mobility attributes. Mobility microdata provides an opportunity to independently/externally validate the mobility attributes of synthetic populations. This study demonstrates a spatially-oriented data validation framework and independent data validation to assess the mobility attributes of two synthetic populations at different spatial granularities. Validation using independent data (SafeGraph) and the validation framework replicated the spatial distribution of errors detected using source data (LODES) and total absolute error. Spatial clusters of error exposed the locations of underrepresented and overrepresented communities. This information can guide bias mitigation efforts to generate a more representative synthetic population. [ABSTRACT FROM AUTHOR]

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 transition probability matrix. Monte Carlo experiments suggest that these estimators are not only quite effective in producing a much less sparse matrix, alleviating issues related to sparsity, but also superior to MLEs in terms of lowering the mean squared error for individual and total transition probability, giving rise to the better recovery of the underlying dynamics. [ABSTRACT FROM AUTHOR]

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 neighborhood delineation that do not require pre-specification of a k-parameter, however, are either nonspatial or lead to noncontiguous or overlapping regions. In this paper, we propose the use of max-p-regions for neighborhood delineation so that the geographic space can be partitioned into a set of homogeneous and geographically contiguous neighborhoods. In addition, we developed a new efficient algorithm to address the computational challenges associated with solving the max-p-regions so that it can be applied for large-scale neighborhood delineation. This new algorithm is implemented in the open-source Python Spatial Analysis Library (PySAL). Computational experiments based on both simulated and realistic data sets are performed and the results demonstrate its effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

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 intelligence, machine learning, and data science as disciplines. The paper also considers the structural changes that are occurring in the organization of science, and the trend toward democratization of spatial analysis and spatial data. I identify critical areas for methodological advances along with some opportunities for the rebranding of GA in the new era of data science. [ABSTRACT FROM AUTHOR]

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 have been sparsely developed. This study introduces a visual analytics system for the space–time analysis of income dynamics. We use state‐level US income data from 1929 to 2009 to demonstrate the visual analytics system and its utility for exploring similar data. The system consists of two modules, visualization and analytics. The visualization module, a Web‐based front‐end called Rank‐Path Visualizer (RPV), draws inspiration from the cartographic technique of flow mapping, originally developed by Tobler and embodied in his canonical Flow Mapper application. [ABSTRACT FROM AUTHOR]

Hypoxia is a universal feature of solid cancers caused by a mismatch between cellular oxygen supply and consumption. To meet the increased demand for oxygen, hypoxic cancer cells (CCs) induce a multifaceted process known as angiogenesis, wherein new vessels are formed by the sprouting of pre-existing ones. In addition to providing oxygen for growth and an exit route for dissemination, angiogenic vessels and factors are co-opted by CCs to enable the generation of an immunotolerant, hypoxic tumor microenvironment, leading to therapeutic failure and mortality. In this review, we discuss how hypoxia-inducible factors (HIFs), the mechanistic target of rapamycin (mTOR), and the unfolded protein response (UPR) control angiogenic factors serving both vascular and immunomodulatory functions in the tumor microenvironment. Possible therapeutic strategies, wherein targeting oxygen sensing might enhance anti-angiogenic and immunologically-mediated anti-cancer responses, are suggested. [ABSTRACT FROM AUTHOR]

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 at the North American Regional Science Council meetings held in Montreal, Canada in November of 2022, followed by an invitation from the editors of Journal of Geographical Systems, Manfred Fischer, Antonio Paez, and Petra Staufer-Steinnocher, to organize a special issue in his honor. Soon thereafter, an open call was initiated to solicit submissions to this special issue, seeking original contributions that overlap and complement the broad range of research undertaken by Professor Getis over a distinguished career and life. This paper offers an overview of prominent geographical systems work carried out by Professor Getis. Reflections on his many contributions are also detailed. A summary of the contributions to this special issue is given, along with final thoughts. [ABSTRACT FROM AUTHOR]

Unlike traditional geodemographic methods that do not incorporate neighborhood change into the delineation process, their method formulates the clustering problem as an integer optimization model to maximize the quality of model predictions regarding neighborhood change. Neighborhoods are increasingly central constructs in the social sciences, serving as spatiotemporal frames for the analysis of a host of questions. Despite the recent renewed interest in neighborhood effects and neighborhood dynamics, these two literatures remain largely separate. [Extracted from the article]

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 at a set of predetermined income levels. Given this comprehensiveness, stochastic dominance measures can yield novel insight into the relationship between income distributions, and thus to the relative economic welfare of inhabitants of different societies. However, stochastic dominance measures used in the analysis of regional income convergence have ignored the complications of geography. In this paper we examine the properties of common tests for stochastic dominance using various descriptive models of geographically‐realistic income growth processes. We find that the false positive rate of stochastic dominance tests increases significantly when comparing spatially‐informed income distributions growing over time. Further, this over‐eager rejection rate requires only that one distribution be spatially‐dependent, unlike some other bivariate statistics. We demonstrate that both the parametric and non‐parametric spatial filtering procedures are effective at resolving these issues in restricted circumstances for higher‐order dominance tests. Overall, while providing a novel and useful perspective on income distribution dynamics, stochastic dominance measures are demonstrated to have undesirable properties when used in geographically‐realistic scenarios and they resist one common method to address these issues. Solutions to the concerns expressed here remain open. [ABSTRACT FROM AUTHOR]

Abstract: The United Nations expressed an interest in reducing subnational (i.e., province and state level) 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 can help measure and reduce regional inequality. To illustrate the use of the SDGC, we first present data for 93 nations to obtain cross‐sectional variation. Next, to illustrate how the SDGC trends over time, changes in the Human Development Index in Mongolia are compared to Russia and China. The SDGC can show improvement, decline and persistent clustering of subnational level inequality. The SDGC is a useful measure for the United Nations' Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

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 levels that exceeded the alarmingly high patterns that existed in the 1920s. Social inequality is characterized by greater mobility than that found for state per capita incomes. Spatial dependence is also distinct between the two series, with per capita incomes exhibiting strong global spatial autocorrelation, while state interpersonal income inequality does not. Local hot and cold spots are found for the per capita income series, while local spatial outliers are found for state interpersonal inequality. Mobility in both inequality series is found to be influenced by the local spatial context of a state. [ABSTRACT FROM AUTHOR]

Molecular oxygen (O 2 ) is a universal electron acceptor that enables ATP synthesis through mitochondrial respiration in all metazoans. Consequently, hypoxia (low O 2 ) has arisen as an organizing principle for cellular evolution, metabolism, and (patho)biology, eliciting a remarkable panoply of metabolic adaptations that trigger transcriptional, translational, post-translational, and epigenetic responses to determine cellular fitness. In this review we summarize current and emerging cell-autonomous molecular mechanisms that induce hypoxic metabolic reprogramming in health and disease. [ABSTRACT FROM AUTHOR]

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 spatial units and high correlations among predictor variables. In this study, we seek to develop an effective framework called spatially filtered ridge regression (SFRR) to estimate the variations in the quantity and distribution of land surface temperature (LST) in response to various land cover patterns. The SFRR effectively integrates spatial autoregressive models and ridge regression, and it achieves reliable parameter estimates with substantially reduced mean square errors. We show this by comparing the performance of the SFRR to other widely adopted models using Monte Carlo simulation followed by an empirical study over central Phoenix. Results highlight the great potential of the SFRR in producing accurate statistical estimates, providing a positive step toward informed and unbiased decision-making across a wide variety of disciplines. (Code and data to reproduce the results in the case study are available at: .) [ABSTRACT FROM AUTHOR]

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, number of desired classes, and form of sampling are shown to have significant impacts on the accuracy of map classifications. Tradeoffs between improved speed of the sampling approaches and loss of accuracy are also considered. The results suggest the possibility of guiding the choice of classification scheme as a function of the properties of large data sets. [ABSTRACT FROM AUTHOR]

Dissemination of breast cancer cells (BCCs) to distant sites (metastasis) is the ultimate cause of mortality in patients with breast cancer. Hypoxia (low O 2 ) is a microenvironmental hallmark of most solid cancers arising as a mismatch between cellular O 2 consumption and supply. Hypoxic selection of BCCs triggers molecular and cellular adaptations dependent upon hypoxia-inducible factors (HIFs), a family of evolutionarily conserved transcriptional activators that coordinate the expression of numerous genes controlling each step of the metastatic process. In this review, we summarize current advances in the understanding of HIF-driven molecular mechanisms that promote BCC metastatic dissemination and patient mortality. In addition, we discuss the clinical and therapeutic implications of HIF targeting in breast cancers. [ABSTRACT FROM AUTHOR]

In this paper we examine the relationship between spatial inequality, place mobility, and spatial polarization in Mexico. We use data for 2457 Mexican municipios from 2000 to 2015. We apply classical and spatial Markov chains to measure spatial income dynamics and we employ a decomposition of spatial inequality to measure spatial polarization. In doing so, we ask the following questions: Has spatial inequality increased across Mexican municipios since 2000? What has been the role of place mobility in any changes in overall spatial inequality? Finally, we examine what the main components of place mobility have been over this period. We find evidence of a strong spatial dependency between municipios that has increased over time but we find no clear pattern at the state scale. Intra-state municipal inequality has relatively declined while Inter-state municipal inequality polarization has increased. We also find clear evidence that transitional dynamics of municipio incomes are influenced by spatial context. Substantial evidence shows that municipio income mobility is driven by a growth component that could be likely reflecting the devaluation of the peso over this period. Exchange mobility is found to be a more important component of overall place mobility than dispersion (inequality) mobility. • Evidence of spatial heterogeneity with high income pockets throughout all regions. • Spatial poverty traps point towards the need to focus on pro-poor regional growth strategies. • Growth mobility accounts for the largest component of income dynamics followed by exchange mobility. [ABSTRACT FROM AUTHOR]

This article explores the stability of local vehicle ownership rates in Great Britain using the technique of spatial Markov chain analysis. Non-spatial Markov chain processes describe the transition of neighbourhoods through levels of ownership with no regard to their neighbourhood context. In reality however, how a neighbourhood transitions to different levels of ownership could be influenced by its neighbourhood context. A spatial Markov chain accounts for this context by estimating transition properties that are conditioned on the surrounding neighbourhood. These spatial Markov chain properties are estimated using a long run census time series from 1971 to 2011 of household vehicle ownership rates in Great Britain. These processes show that there is different behaviour in how neighbourhoods transition between levels of ownership depending on the context of their surrounding neighbours. The general finding is that the spatial Markov process will lead to a greater homogeneity in levels of ownership in each locality, with neighbourhoods surrounded by relatively low ownership neighbourhoods taking longer than a non-spatial Markov process would suggest to transition to higher levels, whilst neighbourhoods of high ownership surrounded by high ownership neighbourhoods take longer to transition to lower levels. This work corroborates Tobler's first law of geography “Everything is related to everything else, but near things are more related than distant things” but also provides practical guidance. Firstly, in modelling ownership, spatial effects need to be tested and when present, accounted for in the model formulation. Secondly, in a policy context, the surrounding neighbourhood situation is important, with neighbourhoods having a tendency towards homogeneity of ownership levels. This allows for the effective planning of transport provision for local services. Thirdly, vehicle ownership is often used as a proxy for the social and aspirational nature of an area and these results suggest that these properties will persist for a prolonged period, possibly perpetuating and exacerbating differentials in society. [ABSTRACT FROM AUTHOR]

Hypoxia (low O 2 ) is an essential microenvironmental driver of phenotypic diversity in human solid cancers. Hypoxic cancer cells hijack evolutionarily conserved, O 2 - sensitive pathways eliciting molecular adaptations that impact responses to radiotherapy, tumor recurrence and patient survival. In this review, we summarize the radiobiological, genetic, epigenetic and metabolic mechanisms orchestrating oncogenic responses to hypoxia. In addition, we outline emerging hypoxia- targeting strategies that hold promise for individualized cancer therapy in the context of radiotherapy and drug delivery. [ABSTRACT FROM AUTHOR]

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. In this paper we examine the properties of tests for spatial effects in DMC models of regional distribution dynamics. We do so through a series of Monte Carlo simulations designed to examine the size, power and robustness of tests for spatial heterogeneity and spatial dependence in transitional dynamics. This requires that we specify a data generating process for not only the null, but also alternatives when spatial heterogeneity or spatial dependence is present in the transitional dynamics. We are not aware of any work which has examined these types of data generating processes in the spatial distribution dynamics literature. Results indicate that tests for spatial heterogeneity and spatial dependence display good power for the presence of spatial effects. However, tests for spatial heterogeneity are not robust to the presence of strong spatial dependence, while tests for spatial dependence are sensitive to the spatial configuration of heterogeneity. When the spatial configuration can be considered random, dependence tests are robust to the dynamic spatial heterogeneity, but not so to the process mean heterogeneity when the difference in process means is large relative to the variance of the time series. [ABSTRACT FROM AUTHOR]

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 concentrated around that reference location. We further introduce an inference approach to identify the statistical significance of a particular segregation value. In an exploratory setting the index can be used to generate a map of hot spots, and seed the question: 'why is this group significantly concentrated around that location?' [ABSTRACT FROM AUTHOR]

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 changes. Rey (2004) introduced a spatial concordance statistic as an extension of Kendall's rank correlation statistic, a commonly employed measure of exchange mobility. This article suggests local forms of the global spatial concordance statistic: local indicators of mobility association (LIMA). The LIMA statistics allow for the decomposition of the global measure into the contributions associated with individual locations. They do so by considering the degree of concordance (stability) or discordance (exchange mobility) reflected within an economy's local spatial context. Different forms of the LIMAs derive from alternative expressions of the neighborhood and neighbor set. Additionally, the additive decomposition of the LIMAs permits the development of a mesolevel analytic to examine whether the overall space–time concordance is driven by either interregional or intraregional concordance. The measures are illustrated in a case study that examines regional income dynamics in Mexico. [ABSTRACT FROM AUTHOR]

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 dynamics fundamentally distinct between Mexico and the United States? Does the role of spatial context influence the distributional dynamics of the two systems? Finally we examine if there is a distinct international border region that displays inequality dynamics different from those of the internal regions of the two national systems. Strong evidence of spatial heterogeneity in regional income mobility is found between the two national systems, with Mexico having higher mobility relative to the US. The international border region is found to have distinct mobility dynamics from either national system, experiencing the strongest mobility. Extensive evidence of spatial contextual effects are found throughout the US-Mexican pooled data set indicating that a region's transitional dynamics are influenced by incomes of neighboring regions. [ABSTRACT FROM AUTHOR]

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 geospatial analytics for decision support. A common thread throughout the discussion is the emphasis on openness, interoperability, and provenance management in a scientific workflow. The code base of the PySAL library provides the common computing framework underlying all delivery mechanisms. [ABSTRACT FROM AUTHOR]

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. They form the link between the data structure in a GIS and the spatial analysis methods. Over time, the number of formats for spatial weights implemented in software has proliferated, without any standard or easy interoperability. In this paper, we propose a flexible format that provides a way to ensure interoperability within a cyberinfrastructure environment. We illustrate the format with an application of a spatial weights web service, which is part of an evolving spatial analytical workbench. We describe an approach to embed provenance in spatial weights structures and illustrate the performance of the web service by means of a number of small experiments. [ABSTRACT FROM PUBLISHER]

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 unpack the aggregate spatiotemporal income dynamic into its contributing components: stability, growth and polarisation, providing a new look at the economic fortunes of diverse neighbourhoods. We examine the relative strength of each component in driving the overall pattern, in addition to whether, how, and why these forces wax and wane across space and over time. Our results show that over the long run, growth is a dominant form of change across all metros, but there is a very clear decline in its prominence over time. Further, we find a growing positive relationship between the components of dispersion and growth, in a reversal of prior trends. Looking across metro areas, we find temporal heterogeneity has been driven by different socioeconomic factors over time (such as sectoral growth in certain decades), and that these relationships vary enormously with geography and time. Together these findings suggest a high level of temporal heterogeneity in neighbourhood income dynamics, a phenomenon which remains largely unexplored in the current literature. There is no universal law governing the changing economic status of neighbourhoods in the US over the last 40 years, and our work demonstrates the importance of considering shifting dynamics over multiple spatial and temporal scales. [ABSTRACT FROM AUTHOR]

A recent review noted important differences in the results of the local Moran's Ii 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 heteroskedasticity, generate these differences. To test this, we examine the null hypotheses implicit in common statistical significance tests of local Moran's Ii. We design an experiment to assess the impact of local heterogeneity on tests conducted under the two most common null hypotheses. In this experiment, we analyze the relationship between measures of local variance, such as the local spatial heteroskedasticity (LOSH) statistic, and components of the local Moran's Ii statistic. We run this experiment with controlled synthetic heteroskedastic data and with uncontrolled real‐world data with varying degrees and patterns of local heteroskedasticity. We show that, in both situations, estimates that use the same null are extremely similar, regardless of estimation method. In contrast, all estimates (regardless of the null) are moderately affected by spatial heteroskedasticity. Ultimately, this article demonstrates that there are important conceptual and computational differences about null hypothesis in local testing frameworks, and these differences can have significant practical implications. Therefore, researchers must be aware as to how their choices may shape the observed spatial patterns. [ABSTRACT FROM AUTHOR]

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 choropleth maps are (1) automated computation of the same class intervals for shading different choropleth maps, (2) dynamic visualization of local variation in a variable, and (3) linking for synchronous exploration of multiple choropleth maps. Since the 1990s, these features have been developed and are now included in many commercial geographic information system (GIS) software packages. However, many choropleth mapping tools include only one or two of the three features described above. On the other hand, freely available mapping tools that support side-by-side multiple choropleth map visualizations are usually desktop software only. As a result, most existing tools supporting multiple choropleth-map visualizations cannot be easily integrated with Web-based and open-source data visualization libraries, which have become mainstream in visual analytics and geovisualization. To fill this gap, we introduce an open-source Web-based choropleth mapping tool called the Adaptive Choropleth Mapper (ACM), which combines the three critical features for flexible choropleth mapping. [ABSTRACT FROM AUTHOR]

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), as high as those recorded on other remote island beaches worldwide. Densities were higher at storm tide lines than high tide lines. Results from our study provide empirical data on the distribution of microplastics on the most populated and visited of the Hawaiian islands. [ABSTRACT FROM AUTHOR]

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 been asymptotically derived, their properties in small sample settings and in the presence of contemporaneous spatial dependence within a regional income system are underexplored. We approach these issues via a series of Monte Carlo experiments that require the proposal of a novel data generating process capable of generating spatially dependent time series given a transition probability matrix and a specified level of spatial dependence. Results suggest that when sample size is small, the mobility estimator is biased while spatial dependence inflates its asymptotic variance, raising the Type I error rate for a one-sample test. For the two-sample test of the difference in mobility between two regional economic systems, the size tends to become increasingly upward biased with stronger spatial dependence in either income system, which indicates that conclusions about differences in mobility between two different regional systems need to be drawn with caution as the presence of spatial dependence can lead to false positives. In light of this, we suggest adjustments for the critical values of relevant test statistics. [ABSTRACT FROM AUTHOR]

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 meta-data of public geosocial datasets. We use Twitter data as a prime example of rich geosocial data. These data are associated with several meta-data attributes. Using this meta-data, we perform a geospatial analysis for the source platform from which a tweet is posted, e.g. from Apple or Android device. Our analysis studies all counties in US connected states over 2 years 2016-2017. We show that market structure at the national level masks substantial variation at the county scale. Moreover, we find strong spatial autocorrelation in platform distribution and market share in the US. In addition, we show interesting changes over the 2 years that motivates further analysis at different spatial and temporal levels. Our results are supported with visual maps of location quotients and market dominance, in addition to formal test results of spatial autocorrelation, and spatial Markov analysis. [ABSTRACT FROM AUTHOR]

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 and patch-based models has been widely adopted to characterize landscape changes at various spatial and temporal scales. Nevertheless, the validity of this type of framework in identifying long-term changes, especially subtle or gradual land modifications is seriously challenged. In this paper, we integrate annual image time series, continuous spatial indices, and non-parametric trend analysis into a spatiotemporal study of landscape dynamics over the Phoenix metropolitan area from 1991 to 2010. We harness local indicators of spatial dependence and modified Mann-Kendall test to describe the monotonic trends in the quantity and spatial arrangement of two important land use land cover types: vegetation and built-up areas. Results suggest that declines in vegetation and increases in built-up areas are the two prevalent types of changes across the region. Vegetation increases mostly occur at the outskirts where new residential areas are developed from natural desert. A sizable proportion of vegetation declines and built-up increases are seen in the central and southeast part. Extensive land conversion from agricultural fields into urban land use is one important driver of vegetation declines. The xeriscaping practice also contributes to part of vegetation loss and an increasingly heterogeneous landscape. The quantitative framework proposed in this study provides a pathway to effective landscape mapping and change monitoring from a spatial statistical perspective. [ABSTRACT FROM AUTHOR]

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 analyses that don't effectively contrast the drivers of energy use. The objective of this study is to use the spatial relationships between demographics, building types, and electricity consumption to form new geographies with less variability for use in residential energy assessment. Using Los Angeles and New York City as case studies, differences in energy use variability within predefined geographies (e.g., census tract) are compared to geographies defined by clustering on socio-technical characteristics. Socio-technical clustering, regardless of the chosen subset of variables, reduces the energy consumption variability over pre-defined geopolitical boundaries with high statistical significance (p<<0.0001). By clustering, intra-regional variability is reduced by 13% in Los Angeles and 29% in New York, therefore improving opportunities for prediction and forecasting. This is the first study to examine the role of spatial boundaries in urban energy assessment. The creation of socio-technical geographies for electricity assessment creates opportunities for improving predictions and forecasts for future sub- and cross-city energy studies. [ABSTRACT FROM AUTHOR]

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 for a school districting approach that can minimize racial or socioeconomic segregation at the district level. In this paper, we develop a spatial optimization model that delineates school attendance zones with the aim of minimizing racial segregation of school district to enable the assessment of the impacts of school attendance zones on the racial segregation of school district. Applications of this model to Riverside Unified School District (RUSD) and San Diego Unified School District (SDUSD) in California, USA show that it is possible to reduce racial segregation by 64% at RUSD and 56% at SDUSD, demonstrating the potential of the proposed model. • Develop a method that assigns students to schools with the goal of minimizing racial segregation. • Highlight the potential of school districts to reduce segregation by adjusting student assignment. • The applications demonstrate a significant reduction of racial segregation. [ABSTRACT FROM AUTHOR]

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 limited in their ability to support real time analysis in big data environments. In this research a multicore computing approach is developed that exploits spatial knowledge through the use of geographic information system functionality to efficiently construct an optimal shortest path. The approach utilizes the notion of a convex hull for iteratively evaluating obstacles and constructing pathways. Further, the approach is capable of incrementally improving bounds, made possible through parallel processing. Wayfinding routes that avoid buildings and other obstacles to travel are derived and discussed. [ABSTRACT FROM AUTHOR]

In this paper, we report efforts to develop a parallel implementation of thep-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 parallel implementation technique that provides a benchmark for more complex implementations of this algorithm. Both the initialization phase, utilizing a Memory-based Randomized Greedy and Edge Reassignment (MERGE) algorithm, and the local search phase, utilizing Simulated Annealing, are distributed over available compute cores. Our results suggest that the proposed parallelization strategy is capable of solving the compactness-driven regionalization problem both efficiently and effectively. We expect this work to advance CyberGIS research by extending its application areas into the regionalization world and to make a contribution to the spatial analysis community by proposing this parallelization strategy to solve large regionalization problems efficiently. [ABSTRACT FROM PUBLISHER]