Use of Remote Sensing for Population Number Determination
| Autor: | R. Schomacker, Gustave Nachigera Mushagalusa, K. Kundert, M. E. Bagula, C. Schmidt, Katcho Karume, O. Azanga, C. Nfundiko, B. F. Safina, N. Karume, D. Ganza |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
education.field_of_study
Data collection 010504 meteorology & atmospheric sciences Population 0211 other engineering and technologies High density Sample (statistics) 02 engineering and technology Medium density Census 01 natural sciences Geography Remote sensing (archaeology) Satellite education Cartography 021101 geological & geomatics engineering 0105 earth and related environmental sciences Demography |
| Zdroj: | The Open Access Journal of Science and Technology. |
| ISSN: | 2314-5234 |
| DOI: | 10.11131/2017/101227 |
| Popis: | Ideally, in a country the population censuses are held regularly (five or ten-year intervals), population surveys, called “control surveys” are then conducted during the intercensal period. The latter, as well as the registers of civil status (information on the movements of the population), help determining a representative sample, called “scale model of the population.” Random, stratified and weighted, it has the advantage of providing a good statistical database for any generalizations about the target population with relatively little risk of error. Our study area, Bukavu city, doesn't comply with the classical scheme of data collection for two main reasons: - there are more than twenty years that real demographic censuses have been carried out in the province, the records of the `civil status is poorly maintained and often incomplete—if any!—Especially during this post-conflict period. A study was conducted in Bukavu City to determine the number of people living in this city. Two GeoEye satellite images of 50 cm resolution captured in July 2012 were used. A net of 200 × 200 meters was created with ArcGIS to divide the satellite images into regular cells. In total 2772 cells were created to cover the two satellite images but only 2353 cells were considered for classification. Three classes were identified in the satellite images according to houses density: High density, medium density and low density zones. Three samples were selected and for each different density type, a point map was created covering each house of the selected sample zones received a point. Using the three different density patterns, 95 highly populated zones were identified, 307 medium density zones and 800 low density zones having each respectively a total of 30'400, 46'050, and 40'000 houses. The population of the city was obtained by taking the number of houses times an average of 8, 7 and 6 habitants per house respectively in high, medium and low density zones. A total of 805550 habitants was obtained for Bukavu city which is almost the same number of people estimated (830'000) by the Inspection Provinciale de la Sante which is the health office in charge of vaccination campaign in South-Kivu Province. This method can be used whenever there is a need to quickly estimate the number of the population in a region where there is no census data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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