BibTex Citation Data :
@article{geoplanning26600, author = {Kamel Allaw and Jocelyne Adjizian Gerard and Makram Chehayeb and Nada Saliba and Abbas Rammal and Zainab Jaber}, title = {Population Estimation Using Geographic Information System and Remote Sensing for Unorganized Areas}, journal = {Geoplanning: Journal of Geomatics and Planning}, volume = {7}, number = {2}, year = {2020}, keywords = {Population estimation; Remote Sensing; GIS; unorganized areas; Lebanon}, abstract = { Population estimation using remotely sensed data has been largely discussed in the literature relative to human geography. However, the previously established models can be applied on organized areas (mainly urban areas) but they are not suitable for unorganized areas which already suffer from a lack of population data. So, the aim of this study is the establish a statistical model for population estimation based on remote sensing data and suitable for unorganized areas. To do so, the morphological characteristics have been studied and a bivariate analysis was carried out to determine factors having a strong relationship with population data as a first step. Second, factors with strongest correlations have been chosen to establish the required model. As a result, an equation has been generated which relates the population data to building volume, density of roads, number of nodes, actual urban areas, and urban trend. }, issn = {2355-6544}, pages = {75--86} doi = {10.14710/geoplanning.7.2.75-86}, url = {https://ejournal.undip.ac.id/index.php/geoplanning/article/view/26600} }
Refworks Citation Data :
Population estimation using remotely sensed data has been largely discussed in the literature relative to human geography. However, the previously established models can be applied on organized areas (mainly urban areas) but they are not suitable for unorganized areas which already suffer from a lack of population data. So, the aim of this study is the establish a statistical model for population estimation based on remote sensing data and suitable for unorganized areas. To do so, the morphological characteristics have been studied and a bivariate analysis was carried out to determine factors having a strong relationship with population data as a first step. Second, factors with strongest correlations have been chosen to establish the required model. As a result, an equation has been generated which relates the population data to building volume, density of roads, number of nodes, actual urban areas, and urban trend.
Article Metrics:
Last update:
Last update: 2024-11-04 08:43:25