Yongmei Lu

Analyzing Relatedness by Toponym Co‐Occurrences on Web Pages

This research proposes a method for capturing “relatedness between geographical entities” based on the co-occurrences of their names on web pages. The basic assumption is that a higher count of co-occurrences of two geographical places implies a stronger relatedness between them. The spatial structure of China at the provincial level is explored from the co-occurrences of two provincial units in one document, extracted by a web information retrieval engine. Analysis on the co-occurrences and topological distances between all pairs of provinces indicates that: (1) spatially close provinces generally have similar co-occurrence patterns; (2) the frequency of co-occurrences exhibits a power law distance decay effect with the exponent of 0.2; and (3) the co-occurrence matrix can be used to capture the similarity/linkage between neighboring provinces and fed into a regionalization method to examine the spatial organization of China. The proposed method provides a promising approach to extracting valuable geographical information from massive web pages.

Detection of dynamic activity patterns at a collective level from large-volume trajectory data

Recent developments in pervasive location acquisition technologies provide the technical support for massive collection of trajectory data. Activity locations identified from trajectory data can be used to evaluate space–time activity patterns. However, the studies that explore activity patterns at collective levels often fail to address the temporal aspect. The traditional spatial statistics, which are commonly used for spatial pattern analysis, are limited in describing space–time interactions. This paper proposes a method to detect the dynamics of space–time development of urban activity patterns that are embedded in large volume trajectory data. Taxi cabs’ trajectory data in the city of San Francisco were analyzed to identify activity instances, activity hot spots, and space–time dynamics of activity hot spots. The urban activity hot spots, evolving through different stages and across the city, provide a comprehensive depiction of the space–time activity patterns in the urban landscape. The dynamic patterns of the activity hot spots can be used to retrieve historical events and to predict future activity hot spots, which may be valuable for transportation and public safety management.

Geospatial techniques in urban hazard and disaster analysis

Sea Level Rise and Flood Analysis.- Modeling Sea-Level Rise and Surge in Low-Lying Urban Areas Using Spatial Data, Geographic Information Systems, and Animation Methods.- Urban Expansion and Sea-Level Rise Related Flood Vulnerability for Mumbai (Bombay), India Using Remotely Sensed Data.- A GIS for Flood Risk Management in Flanders.- Using Geographic Information Science to Estimate Vulnerable Urban Populations for Flood Hazard and Risk Assessment in New York City.- Geo-Information Technology for Infrastructural Flood Risk Analysis in Unplanned Settlements: A Case Study of Informal Settlement Flood Risk in the Nyabugogo Flood Plain, Kigali City, Rwanda.- Metropolitan Case Studies.- A Respiratory Riskscape for Texas Cities: A Spatial Analysis of Air Pollution, Demographic Attributes and Deaths from 2000 Through 2004.- Spatial Distribution of Toxic Release Inventory Sites in Chicago Area: Is There Environmental Inequity?.- Risk and Exposure to Extreme Heat in Microclimates of Phoenix, AZ.- Wildfire Risk Analysis at the Wildland Urban Interface in Travis County, Texas.- Early Warning of Food Security Crises in Urban Areas: The Case of Harare, Zimbabwe, 2007.- Earthquakes, Tsunamis, and International Applications.- Spatial Information Technologies for Disaster Management in China.- A Cybercartographic Tool for Supporting Disaster Prevention Planning Processes and Emergency Management in Mexico City.- Integration of Tsunami Analysis Tools into a GIS Workspace - Research, Modeling, and Hazard Mitigation efforts Within NOAAs Center for Tsunami Research.- Utilizing New Technologies in Managing Hazards and Disasters.- Hurricane Response/Recovery.- Remote Sensing and GIS Data/Information in the Emergency Response/Recovery Phase.- Investigating Recovery Patterns in Post Disaster Urban Settings: Utilizing Geospatial Technology to Understand Post-Hurricane Katrina Recovery in New Orleans, Louisiana.- Space and Time Changes in Neighborhood Recovery After a Disaster Using a Spatial Video Acquisition System.- Evacuation Studies.- Pre-evacuation Trip Behavior.- Micro-Level Emergency Response: 3D Geometric Network and an Agent-Based Model.- A Planning Support System for Terror-Resistant Urban Communities.

Examining Personal Air Pollution Exposure, Intake, and Health Danger Zone Using Time Geography and 3D Geovisualization

Expanding traditional time geography, this study examines personal exposure to air pollution and personal pollutant intake, and defines personal health danger zones by accounting for individual level space-time behavior. A 3D personal air pollution and health risk map is constructed to visualize individual space-time path, personal Air Quality Indexes (AQIs), and personal health danger zones. Personal air pollution exposure level and its variation through space and time is measured by a portable air pollutant sensor coupled with a portable GPS unit. Personal pollutant intake is estimated by accounting for air pollutant concentration in immediate surroundings, individual’s biophysical characteristics, and individual’s space-time activities. Personal air pollution danger zones are defined by comparing personal pollutant intake with air quality standard; these zones are particular space-time-activity segments along an individual’s space-time path. Being able to identify personal air pollution danger zones can help plan for proper actions aiming at controlling health impacts from air pollution. As a case study, this paper reports on an examination and visualization of an individual’s two-day ozone exposure, intake and danger zones in Houston, Texas.

Constructing a Near Real‐time Space‐time Cube to Depict Urban Ambient Air Pollution Scenario

This study adopts a near real-time space-time cube approach to portray a dynamic urban air pollution scenario across space and time. Originating from time geography, space-time cubes provide an approach to integrate spatial and temporal air pollution information into a 3D space. The base of the cube represents the variation of air pollution in a 2D geographical space while the height represents time. This way, the changes of pollution over time can be described by the different component layers of the cube from the base up. The diurnal ambient ozone (O3) pollution in Houston, Texas is modeled in this study using the space-time air pollution cube. Two methods, land use regression (LUR) modeling and spatial interpolation, were applied to build the hourly component layers for the air pollution cube. It was found that the LUR modeling performed better than the spatial interpolation in predicting air pollution level. With the availability of real-time air pollution data, this approach can be extended to produce real-time air pollution cube is for more accurate air pollution measurement across space and time, which can provide important support to studies in epidemiology, health geography, and environmental regulation.

Evaluating Local Non-Stationarity when Considering the Spatial Variation of Large-scale Autocorrelation

Multi-scale effects of spatial autocorrelation may be present in datasets. Given the importance of detecting local non-stationarity in many theoretical as well as applied studies, it is necessary to “remove” the impact of large-scale autocorrelation before common techniques for local pattern analysis are applied. It is proposed in this paper to employ the regionalized range to define spatially varying sub-regions within which the impact of large-scale autocorrelation is minimized and the local patterns can be investigated. A case study is conducted on crime data to detect crime hot spots and cold spots in San Antonio, Texas. The results confirm the necessity of treating the non-stationarity of large-scale spatial autocorrelation prior to any action aiming at detecting local autocorrelation.

Considering Risk Locations When Defining Perturbation Zones for Geomasking

ABSTRACT Geomasking techniques are commonly used to mask the true location information of cases by introducing noise into location data. This study seeks to improve the spatially adaptive random perturbation (SARP) geomasking method by using the actual distribution of the residential addresses (or “risk location”) rather than the people (or “risk population”) to define a perturbation zone. The procedure used in the study also employs a “donut-shaped” perturbation zone, rather than the traditional “pancake-shaped” zone, when displacing a case. The effectiveness of the proposed geomasking methods is assessed in terms of their potential to control for location re-engineering and their ability to maintain the point patterns embedded in the real distribution. The authors conclude that SARP geomasking using the distribution of actual street addresses protects privacy more effectively than geomasking based on population size; the different SARP techniques do not significantly change the clustering patterns on a ...

The Landscape of GIScience Publications 1997–2007: An Empirical Investigation with Latent Semantic Analysis

This article reports on an empirical study of the trends and patterns of research activities in Geographic Information Science (GIScience) during the years 1997–2007. The GIScience research priorities identified by the University Consortium of Geographic Information Science (UCGIS) were used as guidelines to examine the 985 research articles published in six well-recognized academic journals. Latent Semantic Analysis (LSA) was employed to investigate the association among the different GIScience research themes. The spatial and temporal patterns of the association between the publications and the different GIScience themes were examined to show the development of GIScience research during the study period. Furthermore, correlation analyses between the publications were conducted following the LSA results to reveal GIScience research networks, including the networks of the published articles and those formed by the research places. In this article, we applied an approach that was developed within information science to depict what GIS research activities were conducted when and where and how they connect to each other through sharing common research themes. The related findings pave the way for future efforts to describe the paradigm of GIScience as well as the pattern of GIScience research.

Illegal drug cultivation in Mexico: an examination of the environmental and human factors

Patterns of illicit narcotics cultivation are among the understudied topics. Some studies estimate the prevalence of illegal crops using imagery and remote sensing data. These studies rely heavily on the availability and quality of the related images, which is often an issue for many countries known as major drug producers. Using official drug crop eradication data, this study examines the patterns of illegal drug cultivation in Mexico at the municipality level. Species distribution models of ecology were used to guide the selection of environmental variables. A number of sociodemographic variables were incorporated into the model to describe human factors. Global and local models were compared to discern the determinants of marijuana and opium cultivation. Geographically weighted regression was proved overall more effective than global ordinary least square regression despite the spatial variation of its explanation power. The models explained the spatial patterns of opium poppy cultivation are better than those of marijuana cultivation, suggesting the possible presence of more complicated local factors for growing illicit marijuana crops. A number of human factors such as law enforcement, gang activities, and transportation accessibility were found significant for illicit cultivation.

The spatial patterns of adverse health effects of ozone pollution on childhood respiratory diseases in urban Houston

This paper reports on the investigation of the spatial patterns and variations of adverse health effects of ozone pollution on childhood respiratory diseases in Houston, Texas. The study period is June to September of 2001. No significant global relationship exists between ozone pollution and prevalence of childhood respiratory diseases. However, geographically weighted regression (GWR) analysis reveals spatially varied adverse health effect. With the guidance from GWR results, the association between ozone pollution and childhood respiratory disease prevalence is proved to be significant in three sub-regions. Moreover, spatial regression analysis suggests the presence of spatial dependence of the prevalence of childhood respiratory diseases. The spatial variation of the relationship between ozone pollution and childhood respiratory disease prevalence indicates health effects of confounding or intervening factors. The spatial dependency of disease prevalence is related to both the spatial patterns of pollution and those of confounding factors. The findings call for future investigation to examine the factors that might be working together with or against ozone pollution when health effects are concerned. For health practice and management, a set of neighborhood-specific policy, practice, and resource allocation strategies need to be developed to minimize the adverse health effects of ozone pollution.