David W. Wong

Performance‐improving techniques in web‐based GIS

WebGIS (also known as web‐based GIS and Internet GIS) denotes a type of Geographic Information System (GIS), whose client is implemented in a Web browser. WebGISs have been developed and used extensively in real‐world applications. However, when such a complex web‐based system involves the dissemination of large volumes of data and/or massive user interactions, its performance can become an issue. In this paper, we first identify several major potential performance problems with WebGIS. Then, we discuss several possible techniques to improve the performance. These techniques include the use of pyramids and hash indices on the server side to handle large images. To resolve server‐side conflicts originating from concurrent massive access and user interactions, we suggest clustering and multithreading techniques. Multithreading is also used to break down the long sequential, layer‐based data access to concurrent data access on the client side. Caching is suggested as a means to enhance concurrent data access for the same datasets on both the server and the client sides. The technique of client‐side dynamic data requests is used to improve data transmission. Compressed binary representation is implemented on both sides to reduce transmission volume. We also compare the performance of a prototype WebGIS with and without these techniques.

Comparison of spatial interpolation methods for the estimation of air quality data

We recognized that many health outcomes are associated with air pollution, but in this project launched by the US EPA, the intent was to assess the role of exposure to ambient air pollutants as risk factors only for respiratory effects in children. The NHANES-III database is a valuable resource for assessing childrens respiratory health and certain risk factors, but lacks monitoring data to estimate subjects’ exposures to ambient air pollutants. Since the 1970s, EPA has regularly monitored levels of several ambient air pollutants across the country and these data may be used to estimate NHANES subjects exposure to ambient air pollutants. The first stage of the project eventually evolved into assessing different estimation methods before adopting the estimates to evaluate respiratory health. Specifically, this paper describes an effort using EPAs AIRS monitoring data to estimate ozone and PM10 levels at census block groups. We limited those block groups to counties visited by NHANES-III to make the project more manageable and apply four different interpolation methods to the monitoring data to derive air concentration levels. Then we examine method-specific differences in concentration levels and determine conditions under which different methods produce significantly different concentration values. We find that different interpolation methods do not produce dramatically different estimations in most parts of the US where monitor density was relatively low. However, in areas where monitor density was relatively high (i.e., California), we find substantial differences in exposure estimates across the interpolation methods. Our results offer some insights into terms of using the EPA monitoring data for the chosen spatial interpolation methods.

The Modifiable Areal Unit Problem (MAUP)

Even though Gehlke and Biehl (1934) discovered certain aspects of the modifiable areal unit problem (MAUP), the term MAUP was not coined formally until Openshaw and Taylor (1979) evaluated systematically the variability of correlation values when different boundaries systems were used in the analysis. The problem is called “the modifiable areal unit” because the boundaries of many geographical units are often demarcated artificially, and thus can be changed. For example, administrative boundaries, political districts, and census enumeration units are all subject to be redrawn. When data are gathered according to different boundary definitions, different data sets are generated. Analyzing these data sets will likely provide inconsistent results. This is the essence of the MAUP.

Residential Proximity to Industrial Sources of Air Pollution: Interrelationships among Race, Poverty, and Age

ABSTRACT This study builds on earlier work investigating statistical relationships between sociodemographic characteristics of populations and their residential proximity to industrial sources of air pollution. The analysis uses demographic data from the 1990 U.S. Census and industrial site data from the U.S. Environmental Protection Agency (EPA)s 1990 Toxics Release Inventory (TRI). The focus is on examining interactions among race (African Americans and Whites), poverty (above and below household poverty threshold), and age (children from birth to 5 years of age and elderly people 65 years old or older). Results from three different study areas (Kanawha Valley in West Virginia, the Baton Rouge-New Orleans Corridor in Louisiana, and the greater Baltimore metropolitan area in Maryland) suggest there are important interactions among race, poverty, and age that are likely to have consequential ramifications for efforts aimed at investigating issues related to environmental justice. Our results indicate that a substantial proportion of all demographic groups studied live within a mile of the nearest facility, with values ranging from 22% of Whites above poverty in the Baton Rouge-New Orleans Corridor to 60% of African Americans below poverty in Baltimore. Likewise, a substantial proportion of all demographic groups also live within 2 miles of four or more industrial facilities, with values ranging from 16% for Whites above poverty in the Corridor to 70% for African Americans below poverty in Baltimore. In all three study areas, African Americans were more likely than Whites to (1) live in households with incomes below the household poverty line, (2) have children 5 years of age or younger, (3) live closer to the nearest industrial emissions source, and (4) live within 2 miles of multiple industrial emission sources. Findings indicate that, compared with White children, a substantially higher proportion of African-American children 5 years of age or younger lived in poor households that were located in relatively close proximity to one or more industrial sources of air pollution.

Comparing Traditional and Spatial Segregation Measures: A Spatial Scale Perspective1

Measuring the level of segregation often encounters two methodological issues: measures are sensitive to changes in the geographical scale of the data and the effectiveness of the measure in reflecting spatial segregation. Several spatial measures have been suggested to measure spatial segregation, but whether they are more or less sensitive to changes in spatial scale has not been investigated, while some spatial measures are relatively scale-insensitive. Using the 1990 Census data of 30 selected U.S. metropolitan areas, this paper demonstrates that these spatial measures, similar to the aspatial measure, report higher levels of segregation when smaller areal units are used in the analysis. Some spatial measures are even more sensitive to scale changes than aspatial measures. Certain patterns of the scale sensitivity were identified, but no general rules can be formulated. A preliminary explanation of the scale effect on spatial segregation measures is offered.

MEASURING MULTIETHNIC SPATIAL SEGREGATION

Several extensively used segregation measures have known limitations in differentiating various spatial configurations of population distribution. Recently, new segregation indices overcoming this deficiency were introduced, but are only applicable to unrealistic two-group situations. In this paper, I attempt to model multiethnic segregation on the basis of the notions that segregation refers to the spatial separation among ethnic groups and that interaction among population groups can reduce segregation. The level of segregation is reduced when interaction among ethnic groups increases. The spatial index introduced here is based on an existing segregation measure for a multigroup setting. I demonstrate that the proposed index is efficient in distinguishing different spatial patterns of multiethnic population and is able to reflect the potential for spatial interaction among groups. A simulation and an empirical study are used to illustrate the utility of the proposed index.

Effects of DEM sources on hydrologic applications

Digital elevation model (DEM) data have been used to derive hydrological features which serve as inputs to various models. Currently, elevation data are available from several major sources and at different spatial resolutions: the National Elevation Dataset (NED), Shuttle Radar Topography Mission (SRTM) data, and the Light Detection and Ranging (LIDAR) data. This article shows the variability of analysis results from these data sources and of different scales. A standard algorithm in GIS was used to extract river networks from these DEM data for part of Kansas. Extracted networks were compared with the one derived from the National Hydrography Dataset (NHD). Flood simulation results obtained from various sets of DEM data were also compared. Using higher resolution DEMs can derive more accurate river networks, but highest resolution data may not necessarily offer the best results, depending on the resolutions selected to compare the extracted networks. DEM spatial resolution may have minor impacts on flood simulation results, but inundation areas from flood simulations vary significantly across different DEM data sources. Results highlight the caveats on using DEM-derived river network data for hydrologic applications and the difficulties in reconciling differences among elevation data from various sources and of different resolutions.

GEOSTATISTICS AS MEASURES OF SPATIAL SEGREGATION

Traditional measures of segregation, such as the index of dissimilarity, have been criticized as aspatial in nature. Spatial measures of segregation have been proposed, but they are difficult to use. Based on the idea that segregation implies a spatial separation of ethnic groups, the degree of spatial correlation among groups can reflect the level of segregation. This paper suggests that several geostatistical measures, especially the standard deviational ellipse, are effective tools for capturing the spatial characteristics of a population group. By comparing the ellipses of different groups, measures of segregation can be derived. The paper demonstrates this approach to measuring segregation by way of both a simulation and a case study. [Key words: spatial segregation, geostatistics, deviational ellipses, spatial correspondence/correlation.]

Measuring segregation: an activity space approach

While the literature clearly acknowledges that individuals may experience different levels of segregation across their various socio-geographical spaces, most measures of segregation are intended to be used in the residential space. Using spatially aggregated data to evaluate segregation in the residential space has been the norm and thus individual’s segregation experiences in other socio-geographical spaces are often de-emphasized or ignored. This paper attempts to provide a more comprehensive approach in evaluating segregation beyond the residential space. The entire activity spaces of individuals are taken into account with individuals serving as the building blocks of the analysis. The measurement principle is based upon the exposure dimension of segregation. The proposed measure reflects the exposure of individuals of a referenced group in a neighborhood to the populations of other groups that are found within the activity spaces of individuals in the referenced group. Using the travel diary data collected from the tri-county area in southeast Florida and the imputed racial–ethnic data, this paper demonstrates how the proposed segregation measurement approach goes beyond just measuring population distribution patterns in the residential space and can provide a more comprehensive evaluation of segregation by considering various socio-geographical spaces.

An examination of race and poverty for populations living near industrial sources of air pollution

This study examines the sociodemographic characteristics of people living near industrial sources of air pollution in three areas of the United States: (1) the Kanawha Valley in West Virginia; (2) the Baton Rouge–New Orleans corridor in Louisiana; and (3) the greater Baltimore metropolitan area in Maryland. Using data from the 1990 Toxics Release Inventory (TRI) and the 1990 Census, we analyze relationships between variables assumed to be independent, such as location of single or multiple industrial emission sources, and the dependent variables of race (black/white) and poverty status (above/below poverty level). Results from all three study areas are consistent and indicate that African Americans and those living in households defined to be below the established poverty level are more likely, on average, to live closer to the nearest TRI facility and to live within 2 miles of multiple TRI facilities. Conversely, whites and those living in households above the poverty level are more likely, on average, to live farther from the nearest TRI facility and to live within 2 miles of fewer facilities, compared to African Americans and poor people.