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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]

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]

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]

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.

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]

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]