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GIS and Transportation
Published in Dušan Teodorović, The Routledge Handbook of Transportation, 2015
Historically, analysis capabilities in GIS such as those detailed above were thought to be rather basic, lacking quantitative rigor and specification (or statistical significance). The reality is that these approaches are, in fact, more sophisticated than they might appear. Nevertheless, advanced methods continue to be developed, extended and incorporated as standard GIS analysis tools. Examples include various interpolation techniques as well as statistical and geo-statistical/spatial-statistical methods. Some approaches/methods can be found in the commercial GIS packages mentioned above. A number of GIS-based analysis packages exist that provide access to certain analytical approaches. Examples include the following GIS based software: GeoDa, SANET, CrimeStat, SaTScan, Fragstats, GWR, etc.
An exploratory study of place-names in Sinai Peninsula, Egypt: a spatial approach
Published in Annals of GIS, 2018
Nahed T Zeini, Atef M Abdel-Hamid, Amal S Soliman, Ahmed E Okasha
To better visualize the spatial pattern of place-names concentration, a spatial analysis technique, the kernel density estimation (KDE) is used to provide a useful representation of place-names distribution under each category. While, the KDE is useful for mapping the place-names, it is merely descriptive, even randomly distributed place-names may appear to exhibit some local pockets of concentration. The spatial cluster analysis (SaTScan) is then used to detect whether the occurrences of specific types of place-names in some areas are significantly more frequent than other areas. The results show that most types of place-names form a particular spatial pattern in the region and aggregate in certain areas. In addition, the spatial patterns of specific types of names are associated with environmental and human factors.
Geographic pattern of human mobility and COVID-19 before and after Hubei lockdown
Published in Annals of GIS, 2021
T. Edwin Chow, Yusik Choi, Mei Yang, David Mills, Ricci Yue
The spatiotemporal cluster of COVID-19 confirmed cases in China from Jan 1 – Feb 11 was analysed (SaTScan 2020). This research uses SaTScan, a software that can analyse and detect spatial, temporal and space-time cluster by using Kulldorff’s spatial scan statistics. It is designed to perform geographical surveillance of disease, detect statistically significant spatial or space-time disease clusters and model the disease outbreaks by performing prospective real-time or time-periodic disease dispersion. Among the different models available in SaTScan to perform discreate and continuous scan statistics, this study used Poisson model and applied both discrete (discrete Poisson-based model) and continues (continuous Poisson model) scan statistics.