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Exploring Scale Effect Using Geographically Weighted Regression on Mass Dataset of Urban Robbery : Volume Xl-4/W1, Issue 1 (06/05/2013)

By Yavuz, Ö.

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Book Id: WPLBN0004015058
Format Type: PDF Article :
File Size: Pages 8
Reproduction Date: 2015

Title: Exploring Scale Effect Using Geographically Weighted Regression on Mass Dataset of Urban Robbery : Volume Xl-4/W1, Issue 1 (06/05/2013)  
Author: Yavuz, Ö.
Volume: Vol. XL-4/W1, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection, Copernicus Publications
Historic
Publication Date:
2013
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Yavuz, Ö., & Tecim, V. (2013). Exploring Scale Effect Using Geographically Weighted Regression on Mass Dataset of Urban Robbery : Volume Xl-4/W1, Issue 1 (06/05/2013). Retrieved from http://www.worldlibrary.org/


Description
Description: Department of Geographical Information Systems, The Graduate School of Natural and Applied Sciences, Dokuz Eylul University 35160 Buca Izmir, Turkey. Urban geographers have been studying to explain factors influencing crime on cases limited by their study areas. Researchers have a common opinion that explanatory variables modelling crime on those cases might be irrelevant for another one. None of the researchers tested significance of these variables with changing scales of the study area. Because their data did not allow them to study with different scales. This research examines the scale effect with various data from a wide range of data sources. Geographically Weighted Regression (GWR) method is used to explain that effect, after organizing data by Geographical Information System (GIS) technologies. Explanatory variables deduced for district scale are different from those for grid scale. Hence, the explanatory variables may change not only for different geographical areas but also for different scales of the same area.

Summary
EXPLORING SCALE EFFECT USING GEOGRAPHICALLY WEIGHTED REGRESSION ON MASS DATASET OF URBAN ROBBERY

 

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