Establishing a framework for Open Geographic Information science

When conducting research within a framework of Geographic Information Science (GISc), the scientific validity of this work can be argued as highly dependent upon the extent to which the methods employed are reproducible, and that, in the strictest sense, can only be fully achieved by implementing transparent workflows that utilize both open source software and openly available data. After considering the scientific implications of non-reproducible methods, we provide a review of both open source Geographic Information Systems (GIS) and openly available data, before describing an integrated model for Open GISc. We conclude with a critical review of this embryonic paradigm, with directions for future development in supporting spatial data infrastructure.     

The Co‐evolution of Residential Segregation and the Built Environment at the Turn of the 20th Century: A Schelling Model

To what degree does the built environment of cities shape the social environment? In this article we use a Schelling‐like agent‐based model to consider how changes to the built environment of cities relate to changes in residential segregation by income and ethnicity. To develop this model we exploit insights from a high resolution historical GIS which maps 100% of the population of Newark, NJ in 1880. Newark in 1880 had a complex social landscape characterized by areas of significant social and economic segregation and areas of relative integration. We develop a Schelling model capable of reproducing these residential patterns. We use this model to explore the decentralization of housing, a specific phenomenon associated with the demise of the walking city in the late 19th century. Holding agent preferences constant, but allowing the landscape of the Schelling model to evolve in ways that reflect historical changes to the built environment, produces changes to the social landscape that are also consistent with history. Our work suggests that changes in residential segregation do not necessarily imply changes to individual attitudes and preferences. Changes in residential segregation can be generated by changes to the built environment, specifically the geographic distribution of housing.     

The Past,Present, and Future of Geodemographic Research in the United States and United Kingdom

This article presents an extensive comparative review of the emergence and application of geodemographics in both the United States and United Kingdom, situating them as an extension of earlier empirically driven models of urban socio-spatial structure. The empirical and theoretical basis for this generalization technique is also considered. Findings demonstrate critical differences in both the application and development of geodemographics between the United States and United Kingdom resulting from their diverging histories, variable data economies, and availability of academic or free classifications. Finally, current methodological research is reviewed, linking this discussion prospectively to the changing spatial data economy in both the United States and United Kingdom.     

Identifying regions based on flexible user-defined constraints

The identification of regions is both a computational and conceptual challenge. Even with growing computational power, regionalization algorithms must rely on heuristic approaches in order to find solutions. Therefore, the constraints and evaluation criteria that define a region must be translated into an algorithm that can efficiently and effectively navigate the solution space to find the best solution. One limitation of many existing regionalization algorithms is a requirement that the number of regions be selected a priori. The recently introduced max-p algorithm does not have this requirement, and thus the number of regions is an output of, not an input to, the algorithm. In this paper, we extend the max-p algorithm to allow for greater flexibility in the constraints available to define a feasible region, placing the focus squarely on the multidimensional characteristics of the region. We also modify technical aspects of the algorithm to provide greater flexibility in its ability to search the solution space. Using synthetic spatial and attribute data, we are able to show the algorithm’s broad ability to identify regions in maps of varying complexity. We also conduct a large-scale computational experiment to identify parameter settings that result in the greatest solution accuracy under various scenarios. The rules of thumb identified from the experiment produce maps that correctly assign areas to their ‘true’ region with 94% average accuracy, with nearly 50% of the simulations reaching 100% accuracy.     

Evaluating social vulnerability indicators: criteria and their application to the Social Vulnerability Index

Natural Hazards - As a concept, social vulnerability describes combinations of social, cultural, economic, political, and institutional processes that shape socioeconomic differentials in the...     

A New Algorithm for Continuous Area Cartogram Construction with Triangulation of Regions and Restriction on Bearing Changes of Edges

Collective intelligence is the idea that under the right circumstances collections of individuals are smarter than even the smartest individuals in the group, that is a group has an “intelligence” that is independent of the intelligence of its members. The ideology of collective intelligence undergirds much of the enthusiasm about the use of “volunteered” or crowd-sourced geographic information. Literature from a variety of fields makes clear that not all groups possess collective intelligence, this article identifies four pre-conditions for the emergence of collective intelligence and then examines the extent to which collectively generated mapping systems satisfy these conditions. However, the “intelligence” collectively generated maps is hard to assess because there are two difficult to reconcile perspectives on map quality – the credibility perspective and the accuracy perspective. Much of the current literature on user-generated maps focuses on assessing the quality of individual contributions. However, because user-generated maps are complex social systems and because the quality of a contribution is difficult to assess this strategy may not yield an “intelligent” end product. The existing literature on collective intelligence suggests that the structure of groups is more important than the intelligence of group members. Applying this idea to user-generated maps suggests that systems should be designed to foster conditions known to produce collective intelligence rather than privileging particular contributions/contributors. The article concludes with some design recommendations and by considering the implications of collectively generated maps for both expert knowledge and traditional state sponsored mapping programs.