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ISPRS Int. J. Geo-Inf. 2016, 5(3), 23; doi:10.3390/ijgi5030023

Modeling the Relationship between the Gross Domestic Product and Built-Up Area Using Remote Sensing and GIS Data: A Case Study of Seven Major Cities in Canada

1
Department of Civil Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
2
Department of Geomatics, College of Environmental Design, King AbdulAziz University, P.O. Box 80200, Jeddah 21589, Saudi Arabia
3
Ministry of Higher Education, P.O. Box 225085, Riyadh 11324, Saudi Arabia
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 23 November 2015 / Revised: 11 February 2016 / Accepted: 17 February 2016 / Published: 26 February 2016
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Abstract

City/regional authorities are responsible for designing and structuring the urban morphology based on the desired land use activities. One of the key concerns regarding urban planning is to establish certain development goals, such as the real gross domestic product (GDP). In Canada, the gross national income (GNI) mainly relies on the mining and manufacturing industries. In order to estimate the impact of city development, this study aims to utilize remote sensing and Geographic Information System (GIS) techniques to assess the relationship between the built-up area and the reported real GDP of seven major cities in Canada. The objectives of the study are: (1) to investigate the use of regression analysis between the built-up area derived from Landsat images and the industrial area extracted from Geographic Information System (GIS) data; and (2) to study the relationship between the built-up area and the socio-economic data (i.e., real GDP, total population and total employment). The experimental data include 42 multi-temporal Landsat TM images and 42 land use GIS vector datasets obtained from year 2005 to 2010 during the summer season (June, July and August) for seven major cities in Canada. The socio-economic data, including the real GDP, the total population and the total employment, are obtained from the Metropolitan Housing Outlook during the same period. Both the Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) were used to determine the built-up areas. Those high built-up values within the industrial areas were acquired for further analysis. Finally, regression analysis was conducted between the real GDP, the total population, and the total employment with respect to the built-up area. Preliminary findings showed a strong linear relationship (R2= 0.82) between the percentage of built-up area and industrial area within the corresponding city. In addition, a strong linear relationship (R2= 0.8) was found between the built-up area and socio-economic data. Therefore, the study justifies the use of remote sensing and GIS data to model the socio-economic data (i.e., real GDP, total population and total employment). The research findings can contribute to the federal/municipal authorities and act as a generic indicator for targeting a specific real GDP with respect to industrial areas. View Full-Text
Keywords: remote sensing; multi-temporal images; Landsat images; built-up area; NDBI; NDVI; land use; GIS; industrial area; real GDP remote sensing; multi-temporal images; Landsat images; built-up area; NDBI; NDVI; land use; GIS; industrial area; real GDP
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Faisal, K.; Shaker, A.; Habbani, S. Modeling the Relationship between the Gross Domestic Product and Built-Up Area Using Remote Sensing and GIS Data: A Case Study of Seven Major Cities in Canada. ISPRS Int. J. Geo-Inf. 2016, 5, 23.

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