M. M. Yagoub Geography Program, College of HSS Remote Sensing and GIS for Population

Socioeconomic Characteristics Population Estimations Quality of Life Indicators Building Lot Adjacent Amenities Adjacent Hazards

Population estimation can be performed at the local, regional, and national level based on: Counts of individual dwelling units from high resolution satellites such as IKONOS, QuickBird, OrbView Measurement of land areas and land use classification from low resolution satellites such as Landsat, SPOT, RADARSAT, IRS-1C. Population estimation can be performed at the local, regional, and national level based on: Counts of individual dwelling units from high resolution satellites such as IKONOS, QuickBird, OrbView Measurement of land areas and land use classification from low resolution satellites such as Landsat, SPOT, RADARSAT, IRS-1C. Population Estimation from Remote Sensing

Dwelling Unit Estimation Techniques Assumptions: I magery must be of sufficient spatial resolution ( m) to identify individual structures even through tree cover and whether they are residential, commercial, or industrial buildings; Some estimate of the average number of persons per dwelling unit must be available, and It is assumed all dwelling units are occupied. Population Estimation from Remote Sensing

Al Khabisi-Al Ain, UAE Easy to count number of houses

Population estimation from IKONOS image (with 9 persons as an average family size) in Al Ain

Automated building counts

Al Ain 2000 (Landsat) Measurement of land areas and land use classification from low resolution satellites such as Landsat

Quality of living indicators such as house value, median family income, average number of rooms, average rent, education, and income can be estimated by extracting the following variables from high spatial resolution panchromatic and/or color imagery : building size (sq. ft.) lot size (acreage) existence of a pool (sq. ft.) vacant lots per city block frontage (sq. ft.) distance house is set-back from street existence of driveways existence of garages number of autos visible paved streets (%) street width (ft.) health of the landscaping (vegetation index signature) proximity to manufacturing and/or retail activity. Remote Sensing Quality of Living Indicators

Extraction of Building Infrastructure Using Soft-Copy Photogrammetric Techniques

Urban Infrastructure of Rosslyn, Virginia Derived Using Soft-Copy Photogrammetric Techniques

Limitations of using remote sensing for population studies Spatial resolution: the use of very high spatial resolution images bring with them some major problems such as availability in panchromatic mode and large size (storage, processing time) Image classification: The heterogonous nature (mixed pixels) of urban environment and the possibility that identical spectral reflectance values can correspond to very different land uses and functions poses a classification problem 3D nature of urban areas: Imaging 3D buildings from satellites suffers from three problems. The first one is related to displacement of buildings from their true location (relief displacement), the second one is related to obscuring of lower buildings by higher ones, and the third is related to shadow

GIS for Population GIS can be used for modelling the relationship among population variables. The advantages of using GIS for population studies includes: easy manipulation of data, capability to carry out advanced spatial analysis, creation of various maps in a short time, and improving the overall visualization of census data.

Example of GIS attribute for Population

Population: Citizens

Population: non-Citizens

Ratio between citizens and expatriates: AlAin

Population Density in AL Ain

Lorenze Curve showing relationship between total population and total area occupied: Al Ain The Lorenz curve has been used to depict the state of concentration of population. A diagonal line represents an equal distribution, and the greater the deviations of the Lorenz curve from this line, the greater the inequality.

Density Model showing relationship between distance from the CBD and population density :AlAin The basic population density model is based on relating density to distance from the Central Business District (CBD) as a negative exponential function

Population in Al Ain Population variables show that the population is unevenly distributed in Al Ain. The result of an empirical model shows that the overall density of the city is consistent with location theories, i.e., declining population density from the Central Business District (CBD). The trend of higher-income people living in peripheries of cities is evident worldwide as it is in Al Ain. This study is believed to be the first of its kind to integrate remote sensing and GIS for population studies in Al Ain.

Integration of remote sensing with GIS for population The integration of remote sensing with GIS technology for population studies is vital because each has strength in certain aspects related to population studies

References Donnay, J.–P. Barnsley, M. J., and Longley, P. A., Remote sensing and urban analysis, London: Taylor & Francis. John R. Jensen, Remote Sensing of the Environment: An Earth Resource Perspective, Prentice- Hall, Upper Saddle River, NJ (Chapter 12: Remote Sensing The Urban Landscape). Iisaka, J. and Hegedua, E., Population estimates from Landsat imagery. Remote Sensing of Environment, 12: