期刊
【作者单位】
="GeoDa Center for Geospatial Analysis and Computation School of Geographical Science and Urban Planning Arizona State University Tempe AZ USA"
摘要 :
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 ...
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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.
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摘要 :
Change and movement across space and over time are observed in our everyday lives, with people commuting, traveling, communicating, moving, migrating, etc. Understanding how and why such change occurs is important for various reas...
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Change and movement across space and over time are observed in our everyday lives, with people commuting, traveling, communicating, moving, migrating, etc. Understanding how and why such change occurs is important for various reasons, including management of resources, planning for service improvements, detecting whether there are anomalies of some sort, etc. The analysis of spatial information associated with change and movement continues to be supported by a range of techniques, most notably cartography-based exploratory methods. Somewhat lacking, however, are confirmatory and predictive methods to support such analysis. This paper details a suite of approaches implemented in the Python programming language for exploratory analysis, as well as measures that enable statistical testing for pattern significance. Application results for housing movement in an urban region are used to demonstrate the efficacy and functionality of these methods.
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摘要 :
There are many different metrics used to estimate proximity between locations. These metrics are good in some situations and not so good in others, depending on permissible movement behavior. A complicating issue for general metri...
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There are many different metrics used to estimate proximity between locations. These metrics are good in some situations and not so good in others, depending on permissible movement behavior. A complicating issue for general metrics to accurately reflect proximity is the presence of obstacles and barriers prohibiting certain directions of movement. This paper develops a continuous space-based technique for deriving a guaranteed shortest path between two locations that avoids barriers. The problem is formalized mathematically. A solution approach is presented that relies on geographic information system (GIS) functionality to exploit spatial knowledge, making it accessible for use in various kinds of spatial analyses. Results are presented to illustrate the effectiveness of the solution approach and demonstrate potential for general integration across a range of spatial analysis contexts. The contribution of the paper lies in the formal specification of the problem and an efficient GIS-based solution technique.
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摘要 :
Spatial profiling of community food security data can help the targeting of geographic areas and populations most vulnerable to food insecurity. While multiple poverty mapping systems support spatial profiling, they often lack cap...
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Spatial profiling of community food security data can help the targeting of geographic areas and populations most vulnerable to food insecurity. While multiple poverty mapping systems support spatial profiling, they often lack capabilities to disseminate mapping results to a wide range of audiences and to spatially link qualitative data to quantitative analysis. To address these limitations, this study presents a web mapping framework which integrates a variety of publicly available software tools to enable spatial exploration of both quantitative and qualitative data. Specifically, our framework allows online choropleth mapping and thematic data exploration through a mixture of free mapping Application Programming Interfaces (APIs) and open source software tools for spatial data processing and desktop-like user interfaces. The study demonstrates this framework by developing a web prototype for informing food insecurity issues in Bogotá, Colombia. The prototype implementation reveals that the proposed framework facilitates the development of scalable and functionally-extensible mapping systems and the identification of community-specific food insecurity problems (e.g., food kitchens inaccessible from workplaces of low-income residents). This suggests that web-based cartographic visualization using publicly available software tools can be useful for spatial examination of community food insecurity as well as for cost-effective distribution of the resulting map information.
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摘要 :
Climate change is likely to result in increased aridity, lower runoff, and declining water supplies for the cities of the Southwestern United States, including Phoenix. The situation in Phoenix is particularly complicated by the l...
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Climate change is likely to result in increased aridity, lower runoff, and declining water supplies for the cities of the Southwestern United States, including Phoenix. The situation in Phoenix is particularly complicated by the large number of water providers, each with its own supply portfolio, demand conditions, and conservation strategies. This paper details spatial optimization models to support water supply allocation between service provider districts, where some districts experience deficits and others experience surpluses in certain years. The approach seeks to reconcile and integrate projections derived from a complex simulation model taking into account current and future climate conditions. The formulated and applied models are designed to help better understand the expected increasingly complex interactions of providers under conditions of climate change. Preliminary results show cooperative agreements would reduce spot shortages that would occur even without climate change. In addition, they would substantially reduce deficits if climate change were to moderately reduce river flows in Phoenix's major source regions, but have little effect under the most pessimistic scenarios because there are few surpluses available for re-allocation.
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摘要 :
Field-based continuous representation in a geographical information system (GIS) has long been important for describing complex spatially distributed phenomena, such as population, precipitation, air pollution, temperature elevati...
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Field-based continuous representation in a geographical information system (GIS) has long been important for describing complex spatially distributed phenomena, such as population, precipitation, air pollution, temperature elevation and land cover. Though theoretical knowledge and properties of continuous distributions can be employed, such surfaces are generally approximated or abstracted in practice due to a lack of complete information. That is, such surfaces are based on finite spatial samples, which is a practical necessity with regard to the infinite underlying attribute variability. These approximated surfaces are then used in various spatial analyses, yet impacts are not well understood. This article will examine theoretical properties and errors that result in practice when approximated continuous surfaces are relied on in spatial analysis.
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Location modelling is employed in urban and regional planning to site facilities that provide services of some sort. Issues to be considered usually include the number of facilities to locate, where to site those facilities and ho...
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Location modelling is employed in urban and regional planning to site facilities that provide services of some sort. Issues to be considered usually include the number of facilities to locate, where to site those facilities and how demand is to be served. Given the geographic nature of location problems, a key issue is how to represent facilities and demand in geographic space. Traditionally, spatial abstraction as discrete demand is assumed as it simplifies model formulation and reduces computational complexity. However, errors in derived solutions are likely not negligible, especially when demand varies continuously across a region. This paper discusses a single facility location problem that considers demand to be continuously distributed and allows a facility to be located anywhere in space, the continuous Weber problem. An approach for dealing with continuous demand is proposed that is integrated through geographical information system (GIS) functionality. Empirical results highlight the advantages of the developed approach and the importance of solution integration with GIS.
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There exist many facets of error and uncertainty in digital spatial information. As error or uncertainty will not likely ever be completely eliminated, a better understanding of its impacts is necessary. Spatial analytical approac...
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There exist many facets of error and uncertainty in digital spatial information. As error or uncertainty will not likely ever be completely eliminated, a better understanding of its impacts is necessary. Spatial analytical approaches, in particular, must somehow address data-quality issues. This can range from evaluating impacts of potential data uncertainty in planning processes that make use of methods to devising methods that explicitly account for error/uncertainty. To date, little has been done to structure methods accounting for error. This article develops an integrated approach to address data uncertainty in spatial optimization. We demonstrate that it is possible to characterize uncertainty impacts by constructing and solving a new multi-objective model that explicitly incorporates facets of data uncertainty. Empirical findings indicate that the proposed approaches can be applied to evaluate the impacts of data uncertainty with statistical confidence, which moves beyond popular practices of simulating errors in data.
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In this paper we consider urban spatial structure in US cities using a multidimensional approach. We select six key variables (commuting costs, density, employment dispersion and concentration, land-use mix, polycentricity, and si...
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In this paper we consider urban spatial structure in US cities using a multidimensional approach. We select six key variables (commuting costs, density, employment dispersion and concentration, land-use mix, polycentricity, and size) from the urban literature and define measures to quantify them. We then apply these measures to 359 metropolitan areas from the 2000 US Census. The adopted methodological strategy combines two novel techniques for the social sciences to explore the existence of relevant patterns in such multidimensional datasets. Geodesic self-organizing maps (SOM) are used to visualize the whole set of information in a meaningful way, while the recently developed clustering algorithm of the max-p is applied to draw boundaries within the SOM and analyze which cities fall into each of them.
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摘要 :
Spatial profiling of community food security data can help the targeting of geographic areas and populations most vulnerable to food insecurity. While multiple poverty mapping systems support spatial profiling, they often lack cap...
展开
Spatial profiling of community food security data can help the targeting of geographic areas and populations most vulnerable to food insecurity. While multiple poverty mapping systems support spatial profiling, they often lack capabilities to disseminate mapping results to a wide range of audiences and to spatially link qualitative data to quantitative analysis. To address these limitations, this study presents a web mapping framework which integrates a variety of publicly available software tools to enable spatial exploration of both quantitative and qualitative data. Specifically, our framework allows online choropleth mapping and thematic data exploration through a mixture of free mapping Application Programming Interfaces (APIs) and open source software tools for spatial data processing and desktop-like user interfaces. The study demonstrates this framework by developing a web prototype for informing food insecurity issues in Bogotá, Colombia. The prototype implementation reveals that the proposed framework facilitates the development of scalable and functionally-extensible mapping systems and the identification of community-specific food insecurity problems (e. g., food kitchens inaccessible from workplaces of low-income residents). This suggests that web-based cartographic visualization using publicly available software tools can be useful for spatial examination of community food insecurity as well as for cost-effective distribution of the resulting map information.
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