Lan Mu

Spatial Decompositions, Modeling and Mapping Service Regions to Predict Access to Social Programs

Abstract Although social programs intend to provide equal access for all, in the final evaluation, fairness of the distribution of services is usually dictated by location. Measuring and predicting access to social services can help these programs adjust and better accommodate under-served regions. A method is proposed which delineates the service area of providers delivering social services and produces a probability metric that maps the equity of the program of services for each household. We begin with a computationally trivial method for delineating service areas, map the probability of households being served, and propose an adjustment process, an allocation, to level access to services. We argue such methods can serve to better locate service providers and insure equity when implementing social programs.

A Scale-Space Clustering Method: Mitigating the Effect of Scale in the Analysis of Zone-Based Data

Scale-space clustering methods have a variety of applications in image processing, spatial imagery data mining, classification of land uses, identification of seismic belts, pattern recognition, and more. The full potential of these methods, particularly in a socioeconomic context, has not been realized due to its cumbersome mathematical formulations and lack of implementation in a ready-to-use module for wide distribution. The objectives of this article are threefold: to develop a modified scale-space clustering method (MSSC) that accounts for both attribute homogeneity and spatial contiguity, to implement the method in a geographic information system (GIS) program for wide distribution, and to demonstrate its values in a case study. This is coherent with the intent of developing frame-independent and scale-invariant methods, and has important implications in several spatial analysis issues. For instance, it can be used (1) to construct geographic areas with sufficiently large base population to mitigate the small population problem, (2) to mitigate the modifiable areal unit problem (MAUP) in analyzing zone-based data, and (3) to risk less model-building error in using ordinary least squares (OLS) regression as the clustered zones exhibit less spatial autocorrelation. The case study reveals a converging effect of the clustering process, characterized by an exponential function. A metric, level of convergence, is developed to measure the closeness of area objects in terms of both attribute homogeneity and spatial proximity.

Polygon Characterization With the Multiplicatively Weighted Voronoi Diagram

Abstract Many landscape features are represented as polygons in GIS. This paper characterizes polygon shapes with the multiplicatively weighted Voronoi (MW-Voronoi) diagram and improves its understanding. The MW-Voronoi diagrams composition is implemented with topological overlay, growth simulation, and vertex calculation methods. The decomposition is done by reversing a polygon to MW-Voronoi point pairs by segment. It is a new approach to record, characterize, and compare polygons with form and process. The implementation also serves as a geographic education and visualization tool. Applications of the methods are presented with precipitation, fire polygon, and population change data.

Population landscape: a geometric approach to studying spatial patterns of the US urban hierarchy

We present a geometric and graphic approach to studying spatial patterns of urban hierarchy in the US. The multiplicatively weighted Voronoi diagram is found to be effective for visualizing theoretical regions delineated by socio‐economic variables. The population landscape of the continental US demonstrates overall and stepwise patterns reflecting population, neighborhood and distance, with overwhelming influence from huge metropolitan areas. Stepwise exploration and cluster analysis of the spatial pattern reveal an urban hierarchy. Attributes and arrangement are the two important factors of urban hierarchy, with attribute having a stronger local influence and arrangement having a stronger global influence. The study also presents a variation of Zipfs law to visualize the rank‐size distribution from tabular and statistical space to map space.

Error Detection through Consistency Checking

Abstract Following a brief discussion on various aspects of data quality, possible methods are examined for the detection of errors in a spatial database. Using examples, we introduce the consistency checking method based on spatial relationships among neighboring objects and attribute relationships among map layers from different sources. Using logical relationships among spatial neighborhoods and among attribute data from different sources, it is desirable to build an error detection mechanism in a spatial database. This mechanism can be automated and has the potential to be one of the powerful tools for error detection and correction suggestion in a spatial database.

Quantitative analysis of the driving forces causing declines in marsh wetland landscapes in the Honghe region, northeast China, from 1975 to 2006

The Sanjiang Plain has the most representative and largest concentration of inland freshwater wetlands in China, most of which have been degraded or have disappeared as a result of agricultural development and climatic change since the 1950s. To better understand the spatial and temporal variation and driving forces of marsh reduction, this study investigated variations of marsh reduction in the Honghe region of the Sanjiang Plain, Northeast China over a 30-year period, and analyzed the role of the different driving forces separately and their combined effect on marsh reduction and identified what driving forces have played key roles on the reduction in different periods. Nine natural and anthropogenic variables from remote sensing, GIS data and field surveys, such as precipitation, temperature, precipitation anomaly, population density, agricultural population density, per capita GDP, distance to road, distance to canal and distance to settlement, were evaluated on their impact on observed variations of marsh reduction between 1975 and 2006. The results show that all of these driving forces have significant influences on the decline of the marsh area, and the combination of driving forces that has crucial impacts on marsh reduction varied largely from 1975 to 2006. During 1975–1989, it was the construction of canal and road networks in farms and changes in average annual precipitation that led to marsh reduction. After 1989, the reduction was mainly related to increases in agricultural population, per capita GDP and settlements. These findings may help understand the declines or degradation of marsh areas and provide an empirical and theoretical base for managers, who design and implement wetland management and planning.

A Place-Oriented, Mixed-Level Regionalization Method for Constructing Geographic Areas in Health Data Dissemination and Analysis

Similar geographic areas often have great variations in population size. In health data management and analysis, it is desirable to obtain regions of comparable population by decomposing areas of large population (to gain more spatial variability) and merging areas of small population (to mask privacy of data). Based on the Peano curve algorithm and modified scale-space clustering, this research proposes a mixed-level regionalization (MLR) method to construct geographic areas with comparable population. The method accounts for spatial connectivity and compactness, attributive homogeneity, and exogenous criteria such as minimum (and approximately equal) population or disease counts. A case study using Louisiana cancer data illustrates the MLR method and its strengths and limitations. A major benefit of the method is that most upper level geographic boundaries can be preserved to increase familiarity of constructed areas. Therefore, the MLR method is more human-oriented and place-based than computer-oriented and space-based.

A shape-based buffering method

Distance constraint is a major concern in many spatial analyses. Buffering is one of the proximity techniques in GIS most commonly used to address this constraint. I introduce shape-based point buffering, an anisotropic and variable-distance buffer generation method conformal to the original polygons. In contrast with isotropic fixed-distance buffering, shape-based buffering is defined using a relative distance (percentage) instead of a real unit (for example, meters), and it allows all buffered boundaries to be formed at the same time. The construction and implementation of the buffering method are described below. Three emergency-response scenarios are designed to demonstrate potential applications of this buffering method, including a shape-based fixed-percentage buffer calculation and space distribution, a shape-based variable-percentage buffer region, and a reverse calculation of the task schedule from a probability surface constructed from shape-based buffers. Limitations of the method are discussed. The method has potential applications in emergency preparedness and planning to better address fairness issues when a geographic area must be zoned arbitrarily.

An empirical comparison of spatial demand representations in maximal coverage modeling

Spatial demand representation is critical for applying location models to planning processes and the efficiency of modeling solutions. Current research has focused primarily on assessing and mitigating demand representation error but ignored the computational complexity of implementing demand representations and solving the associated models. We first use set theory to formulize Cromley et als (Institutional Journal of Geographical Information Science 26 495-512) demand representation with the least common demand coverage unit (LCDCU). Then, in the application of using the maximal covering location problem (MCLP) to site base stations optimally for a cellular network, we compare the LCDCU-based representation with widely used point-lattice-based and polygon-lattice-based demand representations in terms of both computational complexity and representation error. The LCDCU-based representation creates demand objects by partitioning a demand space into potential service areas, and has several advantages including offering solutions that provide real 100% demand coverage and eliminating some errors associated with other demand representations. However, the computational complexity of implementing the LCDCU-based representation could easily become extremely high as the number of potential facility sites increases, which could be a challenge to current geographic information systems. In addition, unlike point-based and polygon-based demand representations, the LCDCU-based representations cannot be applied to the planar covering location problems where a facility can be sited anywhere. The results of our study suggest that point-lattice-based demand representations with fine granularity are a good alternative to the LCDCU-based representations due to their effective modeling solutions without extensive computation. Polygon-lattice-based demand representations are not recommended owing to both high computational complexity and relatively large representation error. This study provides some indicators on how to choose an appropriate spatial demand representation in practical applications.

A weighted difference barrier method in landscape genetics

Identifying barriers of species and characterize their effects on spatial distribution provide essential information to research in landscape genetics. We propose a weighted difference barrier (WDB) method as an alternative to maximum difference barriers (MDB), and to initiate and integrate more spatial modeling and methods into the problem solving process. Overall, WDB provides quick and straightforward improvements to the drawbacks of MDB. WDB integrates more sample location relationships into the barrier construction and reveals potential barriers that would otherwise go undetected. WDB incorporates both within group and between group genetic information, and delineates the barriers as a more complex pattern.