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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]

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]

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]

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]

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]

The article discusses various reports published within the issue, including one by Luc Anselin, Ibnu Syabri and Youngihn Kho which provides an overview of GeoDa, a free program supporting interactive exploratory spatial data analysis and another by Roger Bivand which provides an overview of work implementing functions for spatial statistical analysis in the open-source R language environment.

Space–Time Analysis of Regional Systems (STARS) is an open-source package designed for the dynamic exploratory analysis of data measured for areal units at multiple points in time. STARS consists of four core analytical modules: Anselin (1995) exploratory spatial data analysis; Anselin (2003) inequality measures; Carlino and Mills (1993) mobility metrics; and Christakos, Bogaert, and Serre (2001) spatial Markov. Developed using the Python object-oriented scripting language, STARS lends itself to three main modes of use. Within the context of a command line interface (CLI), STARS can be treated as a package which can be called from within customized scripts for batch-oriented analyses and simulation. Alternatively, a graphical user interface (GUI) integrates most of the analytical modules with a series of dynamic graphical views containing brushing and linking functionality to support the interactive exploration of the spatial, temporal, and distributional dimensions of socioeconomic and physical processes. Finally, the GUI and CLI modes can be combined for use from the Python shell to facilitate interactive programming and access to the many libraries contained within Python. This article provides an overview of the design of STARS, its implementation, functionality, and future plans. A selection of its analytical capabilities are also illustrated that highlight the power and flexibility of the package. [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]