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.
Highlights
With respect to the mature development of remote sensing techniques, there emerge several studies using remote sensing data to model the real gross domestic product (GDP) at a national scale
This study aims to investigate the relationship between the built-up area, as well as three socio-economic parameters in order to facilitate any new city development and regional planning, with a case study of seven major cities in Canada
Since not all of the cities have a comprehensive Geographic Information System (GIS) land use dataset to find out the built-up areas, we proposed to utilize remote sensing data to estimate the built-up areas to achieve such a goal
Summary
Satellite remote sensors acquire images of the Earth’s surface by recording the reflected energy from objects on the ground. Remote sensing image classification techniques have been used to aid in identifying the land use/land cover areas [1,2]. Applications of remote sensing, in socio-economic studies, aim to map the spatial extent [4], urban populations [5], intra-urban population density [6,7] and economic activities [8]. These data can provide valuable information for the municipal authorities and researchers to aid in urban planning and city management. In Canada, the gross national income (GNI) mainly depends on the mining and manufacturing industries and services [9]. With respect to the mature development of remote sensing techniques, there emerge several studies using remote sensing data to model the real GDP at a national scale
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