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Application of Spatial Autocorrelation Techniques in Spatial-Temporal Analysis of Domestic Water Consumption in the City of Qom at the Household Level

Document Type : Original Article

Authors

1 M.Sc. Student of Remote Sensing & GIS Dep., Tarbiat Modarres University

2 Associate Prof. of Remote Sensing & GIS Dep.,Tarbiat Modarres University

3 Ph.D. Student of Remote Sensing & GIS Research Center, Shahid Beheshti University

Abstract

Due to the scarcity and crisis of water resources, the issue of optimal use and management of it is of
particular importance. Improper pattern of water consumption in different areas of a city can be one of
the cases that cause water crisis in a city. Therefore, there is necessary to apply methods in order to
identify consumption patterns in different areas of the city. The purpose of this study is to investigate
the spatial pattern of water consumption in Qom city using spatial autocorrelation techniques. For this
reason, the consumption of 117 neighborhoods of Qom city during 2017 was collected and the
average household water consumption for each neighborhood was calculated. Moran index was used
to identify the type of consumption pattern and local Moran index and hot spot technique were used
for spatial distribution of the consumption pattern. The results of spatial autocorrelation showed that
the largest cluster pattern of water consumption in Qom city occurred in summer with the value of
Moran index (I = 0.24). Also, the highest significance of the index (z = 7.02) was observed in this
season. In both local and hot spot analysis, it was observed that high consumption has a high cluster
pattern compared to low consumption. Spatially, high consumption clusters were observed in the
central and western neighborhoods of the city and low consumption clusters were observed in the
southern, eastern and northern neighborhoods of the city. Temporally, high consumption clusters were
observed in central and western neighborhoods in summer and winter, respectively and low
consumption clusters were observed in cold seasons.

Keywords

References
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Iranian Journal of Remote Sensing & GIS
Volume 13, Issue 4 - Serial Number 52
February 2022
Pages 101-118
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