The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-4/W6-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 185–192, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-185-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 185–192, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-185-2021

  18 Nov 2021

18 Nov 2021

FORECASTING URBAN POPULATION DISTRIBUTION OF ILOILO CITY USING GIS AND SPATIAL AUTOCORRELATION MODELS

L. Hilario, J. A. Duka, M. I. Mabalot, J. Domingo, K. A. Vergara, M. J. Villanueva-Jerez, K. A. Cabello, G. A. Rufino, and C. J. Sarmiento L. Hilario et al.
  • UP Training Center for Applied Geodesy and Photogrammetry, University of the Philippines Diliman, Quezon City, Philippines

Keywords: Population Forecasting, Small area, GIS, Spatial Statistics, Spatial Autocorrelation Models, Smart City

Abstract. Rapid urbanization in localities offers a lot of opportunities but also imposes a lot of challenges due to its direct relationship to population growth. This leads to an increase in the demand for essential goods and services such as food, energy, water among others. Hence, small-area population forecasts have long been an important element in urban and regional planning to aid in the decision-making processes in a locality. The promise of smart cities, through the use of advanced technologies, is to make cities livable and sustainable, preparing more opportunities and addressing challenges on urbanization. This study aims to forecast population distribution in Iloilo city by incorporating GIS techniques and highlighting the use of spatial autocorrelation models. The spatial interaction effects between neighboring barangays are taken into consideration to identify a set of factors affecting the population. The results identified a set of significant explanatory variables and whether it will result in an increase or decrease in population. The study also illustrates the resulting population forecast comparing it to the actual total population of the city.