Shino Shiode

The SANET Toolbox: New Methods for Network Spatial Analysis

This paper describes new methods, called network spatial methods, for analysing spatial phenomena that occur on a network or alongside a network (referred to as network spatial phenomena). First, the paper reviews network spatial phenomena discussed in the related literature. Second, the paper shows the uniform network transformation, which is used in the study of non-uniform distributions on a network, such as the densities of traffic and population. Third, the paper outlines a class of network spatial methods, including nearest neighbor distance methods, K-function methods, cell count methods, clumping methods, the Voronoi diagrams and spatial interpolation methods. Fourth, the paper shows three commonly used computational methods to facilitate network spatial analysis. Fifth, the paper describes the functions of a GIS-based software package, called SANET, that perform network spatial methods. Sixth, the paper compares network spatial methods with the corresponding planar spatial methods by applying both methods to the same data set. This comparison clearly demonstrates how different conclusions can result. The conclusion summarizes the major findings.

Spatial analysis of roadside Acacia populations on a road network using the network K-function

Spatial patterning of plant distributions has long been recognised as being important in understanding underlying ecological processes. Ripley’s K-function is a frequently used method for studying the spatial pattern of mapped point data in ecology. However, application of this method to point patterns on road networks is inappropriate, as the K-function assumes an infinite homogenous environment in calculating Euclidean distances. A new technique for analysing the distribution of points on a network has been developed, called the network K-function (for univariate analysis) and network cross K-function (for bivariate analysis). To investigate its applicability for ecological data-sets, this method was applied to point location data for roadside populations of three Acacia species in a fragmented agricultural landscape of south-eastern Australia. Kernel estimations of the observed density of spatial point patterns for each species showed strong spatial heterogeneity. Combined univariate and bivariate network K-function analyses confirmed significant clustering of populations at various scales, and spatial patterns of Acacia decora suggests that roadworks activities may have a stronger controlling influence than environmental determinants on population dynamics. The network K-function method will become a useful statistical tool for the analyses of ecological data along roads, field margins, streams and other networks.

Detection of multi-scale clusters in network space

This paper proposes a new type of point‐pattern analytical method, Network‐Based Variable‐Distance Clumping Method (NT‐VCM), to analyse the distribution pattern of point objects and phenomena observed on a network. It is an extension of Planar Variable‐Distance Clumping Method (PL‐VCM) that was previously defined for point pattern analysis in Euclidian space. The purpose for developing NT‐VCM is to identify point agglomerations across different scales called multi‐scale network‐based clumps among distributed points along a network. The paper first defines a network‐based clump as a set of points where all its elements are found within a certain shortest‐path distance from at least one other element of the same set. It then proposes NT‐VCM as a technique to extract statistically significant multi‐scale clumps on a network. The paper also proposes an efficient algorithm for computing NT‐VCM, which involves the use of the Voronoi diagram, the Delaunay diagram and the minimum spanning tree that are adapted and newly extended for the purpose of analysis on a network. A comparative study of NT‐VCM and PL‐VCM using commercial facility data reveals a notable difference in the location as well as the size of the significant multi‐scale clumps detected in the both cases. Results from the empirical study confirm that NT‐VCM accounts for the actual network distance between the points, thus providing a more accurate description of point agglomerations along the network than PL‐VCM does.

Street-level spatial scan statistic and STAC for analysing street crime concentrations

This study develops new types of hotspot detection methods to describe the micro-space variation of the locations of crime incidents at the street level. It expands on two of the most widely used hotspot detection methods, Spatial and Temporal Analysis of Crime and Spatial Scan Statistic, and applies them to the analysis of the network space. The study first describes the conceptual and the methodological framework of the new methods followed by analyses using: (1) a simulated distribution of points along the street network; and (2) real street-crime incident data. The simulation study using simulated point distributions confirms that the proposed methods is more accurate, stable and sensitive in detecting street-level hotspots than their conventional counterparts are. The empirical analysis with real crime data focuses on the distribution of the drug markets and robberies in downtown Buffalo, NY in 1995 and 1996. The drug markets are found to form hotspots that are dense, compact and stable whereas hotspots of the robberies are observed more thinly across a wider area. The study also reveals that the location of the highest risk remains on the same spot over time for both types of crimes, indicating the presence of hotbeds which requires further attention.

Network-based space-time search-window technique for hotspot detection of street-level crime incidents

This study proposes a street-level space‐time hotspot detection method to analyse crime incidents recorded at the street-address level and provides description of the micro-level variation of crime incidents over space and time. It expands the notion of search-window techniques widely used in crime science by developing a method that can account for the spatial‐temporal distribution of crime incidents measured in network distance. The study first describes the methodological framework by presenting the concept of a new type of search window and how it is used in the process of statistical testing for detecting crime hotspots. This is followed by analyses using (1) a simulated distribution of points along the street network, and (2) a set of real street-crime incident data. The simulation study demonstrates that the proposed method is effective in identifying space‐time hotspots, which include those that are not detected by a non-temporal method. The empirical analysis of the drug markets and assaults in downtown Buffalo, New York, revealed a detailed space‐time signature of each type of crime, highlighting the recurrent nature of drug dealing at specific locations as well as the sporadic tendency of assault incidents.

Street-level Spatial Interpolation Using Network-based IDW and Ordinary Kriging

This study proposes network-based spatial interpolation methods to help predict unknown spatial values along networks more accurately. It expands on two of the commonly used spatial interpolation methods, IDW (inverse distance weighting) and OK (ordinary kriging), and applies them to analyze spatial data observed on a network. The study first provides the methodological framework, and it then examines the validity of the proposed methods by cross-validating elevations from two contrasting patterns of street network and comparing the MSEs (Mean Squared Errors) of the predicted values measured with the two proposed network-based methods and their conventional counterparts. The study suggests that both network-based IDW and network-based OK are generally more accurate than their existing counterparts, with network-based OK constantly outperforming the other methods. The network-based methods also turn out to be more sensitive to the edge effect, and their performance improves after edge correction. Furthermore, the MSEs of standard OK and network-based OK improve as more sample locations are used, whereas those of standard IDW and network-based IDW remain stable regardless of the number of sample locations. The two network-based methods use a similar set of sample locations, and their performance is inherently affected by the difference in their weight distribution among sample locations.

Integrating GIS and Spatial Analytical Techniques in an Analysis of Road Traffic Accidents in Serbia

Road safety issue is drawing major attention of local Serbian authorities who seeks the reduction of the volume of Road Traffic Accidents. The study starts with a summative review of the problem with a focus on the situation in the European Union member countries and the Western Balkan (WB) countries: Albania, Bosnia and Herzegovina, Croatia, Montenegro, the former Yugoslav Republic of Macedonia, Kosovo and Serbia. It then interprets traffic accident data using network K-function and Network Kernel Density Estimation. Unlike the conventional planar methods, the network methods analyze the spatial patterns of accident locations within the network space and are therefore not affected by the configuration of the street network or its distance. The Network K-function helped authors to investigate the presence of clusters, whereas the Network Kernel Density method helped identify the actual cluster locations. Multiple linear regression analysis was also used to find the most significant variables related to the road conditions, time and the main cause which have likely contributed to high rates of accidents. The empirical analysis was carried out using traffic data from the City of Belgrade. Implications for further research were discussed, suggesting that the findings can be used for more in-depth studies aimed at identifying the most significant cause of road accidents in any given area and that they could be extended and applied in other countries of the region.

A Toolbox for Spatial Analysis on a Network

Some of you who heard my presentation in the last GIScience meeting might think this tile looks similar to the last title. Actually it looks similar but not exactly the same. The last time, the title was TOWARD a toolbox for spatial analysis on a network. Two years have passed since then, and our project has progressed, but we have not yet completed this project. Today I am going to present the latest outcome of this project. This project has been conducted by Okunuki, Funamoto, Ishitomi and me. Today, I, Okabe, will talk.

Urban and rural geographies of aging: a local spatial correlation analysis of aging population measures

The spatial distribution of aging populations is commonly measured with either the aging population ratio or the aging population density. Used in isolation, however, these measures may fail to detect aging communities in certain types of urban or rural setting. This study uses both indices simultaneously to identify types and locations of aging communities more accurately. We investigate the spatial distribution of these communities using a standard correlation analysis and bivariate local spatial statistic analysis. Empirical analysis of geospatial data of the Aichi Prefecture in Japan suggests that using both indices allows us to capture different types of aging communities in diverse contexts (e.g. depopulated rural areas, pockets of aging communities in urban areas, and growing concentrations of aging population in the suburbs). The analysis uses data sets aggregated at different areal scales, confirming the generally stable nature of the outcome, despite some scale sensitivity.

Geography of urban ageing and rural ageing: a local spatial correlation analysis of the ageing population ratio in relation to the ageing population density

The spatial distribution of aging populations is commonly measured with either the aging population ratio or the aging population density. Used in isolation, however, these measures may fail to detect aging communities in certain types of urban or rural setting. This study uses both indices simultaneously to identify types and locations of aging communities more accurately. We investigate the spatial distribution of these communities using a standard correlation analysis and bivariate local spatial statistic analysis. Empirical analysis of geospatial data of the Aichi Prefecture in Japan suggests that using both indices allows us to capture different types of aging communities in diverse contexts (e.g. depopulated rural areas, pockets of aging communities in urban areas, and growing concentrations of aging population in the suburbs). The analysis uses data sets aggregated at different areal scales, confirming the generally stable nature of the outcome, despite some scale sensitivity.