Exploratory Spatial Data Analysis Techniques Research Articles

Application and Integration of Lattice Data Analysis, Network K-Functions, and Geographic Information System Software to Study Ice-Related Crashes

Advances in geographic information system (GIS) software and exploratory spatial data analysis (ESDA) techniques give transportation safety engineers tools to observe and analyze safety-related data from a new perspective. This research takes the use of GIS software and ESDA techniques one step further by incorporating advanced statistical techniques for a more thorough and complex analysis of safety data. This is achieved by implementing a network-constrained cross K-function to analyze the relationship between bridges and the occurrences of ice-related crashes within a county. The counties in Wisconsin included in the analysis were selected through the use of the local Moran's I statistic; this statistic allows for the selection of counties within the same geographical area, which have similar parameters (in this case, ice-related crash rates). The objective of this research is to explore the relationship between ice-related crashes and bridges in counties that display similar ice-related crash rates, to compare and analyze winter maintenance techniques. The results identify clustering of ice-related crashes around bridges in four counties with similar ice-related crash rates in southeast Wisconsin. Similarly, two of four counties show clustering of ice-related crashes around bridges in northwest Wisconsin. These results make a strong case to suggest that counties in these regions should focus additional winter maintenance efforts at bridge locations. In addition, this research shows how the use of advanced spatial statistical techniques, particularly network-based statistics applied within a GIS environment, can be used as a unique and innovative approach toward safety data analysis.

Measuring Neighbourhood Spatial Accessibility to Urban Amenities: Does Aggregation Error Matter?

Neighbourhood spatial accessibility (NSA) refers to the ease with which residents of a given neighbourhood can reach amenities. NSA indicators have been used to inform urban policy issues, such as amenity provision and spatial equity. NSA measures are, however, susceptible to numerous methodological problems. We investigate one methodological issue, aggregation error, as it relates to the measurement of NSA. Aggregation error arises when, for the purpose of distance calculations, a single point is used to represent a neighbourhood, which in turn represents an aggregation of spatially distributed individuals. NSA to three types of recreational amenities (playgrounds, community halls, and leisure centres) in Edmonton, Alberta, Canada is used to assess whether aggregation error affects NSA measures. The authors use exploratory spatial data analysis techniques, including local indicators of spatial association, to examine aggregation-error effects on NSA. By integrating finer resolution data into NSA measures, we demonstrate that aggregation error does affect NSA indicators, but that the effect depends on the type of amenity under investigation. Aggregation error is particularly problematic when measuring NSA to amenities that are abundant and have highly localized service areas, such as playgrounds. We recommend that, when analyzing NSA to these types of amenities, researchers integrate finer resolution data to indicate the spatial distribution of individuals within neighbourhoods better, and hence reduce aggregation error.

Exploratory Spatial Data Analysis Techniques for Examining Urban Crime: Implications for Evaluating Treatment

Regional planners, policy makers and policing agencies all recognize the importance of better understanding the dynamics of crime. Theoretical and application-oriented approaches which provide insights into why and where crimes take place are much sought after. Geographic information systems and spatial analysis techniques, in particular, are proving to be essential for studying criminal activity. However, the capabilities of these quantitative methods continue to evolve. This paper explores the use of geographic information systems and spatial analysis approaches for examining crime occurrence in Brisbane, Australia. The analysis highlights novel capabilities for the analysis of crime in urban regions.

Are the environmental niches of Vibrio cholerae 0139 different from those of Vibrio cholerae 01 El Tor?

Background:, Vibrio cholerae are known to be normal inhabitants pf surface water. However, the environmental niches of the different strains of cholera are not well known, and therefore, populations at risk for cholera outbreaks cannot be clearly identified. Methods: This study identifies environmental risk factors for cholera caused by V. cholerae 01 El Tor and 0139 and environmental niches of the two strains present in Matlab, a cholera endemic area of Bangladesh. The study year was 1993, the year that the O139 strain first appeared in the study area. Patients who had either strain of cholera identified in a laboratory were included in the study. A geographic information system was used to map the household locations of the patients, to describe the human sanitary environment and population density, and to address potential anthropogenic and environmental risk factors of the disease. Spatial point pattern and exploratory spatial data analysis techniques were used to define the environmental niches of the two cholera strains. Results: The study suggests the niches of O1 El Tor and O139 strains of V. cholerae appear to be similar, based on common environmental risk factors. Conclusions: The results of this study support a theory that O1 El Tor could possibly be replaced by the newer O139 strain in the future.

Research report: GIS‐oriented analysis of tourist time‐space patterns to support sustainable tourism development

Tourism and tourism development create major changes in the environment. To determine their impact on environmental sustainability, it is necessary to understand tourist behaviour. Time, space and context are important components in describing tourist time‐space behaviour. Tourist time‐space analysis requires specific data about a visitor's time spent, locations visited, routes chosen, information used, perception and motivation. To date, most researchers have analysed spatial tourism data with statistics. This approach requires data for each period and for each possible location in the region. The enormous amount of data and tables makes it difficult to see spatial relations. Geographical information systems are specifically designed to handle spatial relationships. Four types of relevant data can be distinguished: (1) tourist characteristics, (2) actual time‐space behaviour, (3) landscape elements and tourist locations (accommodations, cities/villages, facilities, areas) and (4) images added to these locations. To analyse time‐space behaviour, a method is proposed consisting of two steps: (1) obtain an overall insight into the use of the physical environment by tourists, and apply exploratory spatial data analysis techniques and dynamic cartography; and (2) construct and analyse tourist recreation complexes using network analysis techniques. Pattern analysis is undertaken with mapping and graphing techniques and accessibility studies for actual and potential approximations of the links and nodes in the network. The results of these analyses can be applied to achieve more sustainable planning.

Exploratory Spatial Data Analysis

The paper describes SAGE, a software system that can undertake exploratory spatial data analysis (ESDA) held in the ARC/INFO geographical information system. The aims of ESDA are described and a simple data model is defined associating the elements of ‘rough’ and ‘smooth’ with different attribute properties. The distinction is drawn between global and local statistics. SAGE's region building and adjacency matrix modules are described. These allow the user to evaluate the sensitivity of results to the choice of areal partition and measure of interarea adjacency. A range of ESDA techniques are described and examples given. The interaction between the table, map and graph drawing windows in SAGE is illustrated together with the range of data queries that can be implemented based on attribute values and locational criteria. The paper concludes with a brief assessment of the contribution of SAGE to the development of spatial data analysis.