Simulation Of High-resolution Urban Population Density

The governments at all levels depend on the population data to make all kinds of decisions. It plays an important role in managing the resources, allocating the resources, and the laying of commercial outlets, goods and labor allocation. Nowadays, population census has become an important way to predict the population data. However, the population census was only conducted once every ten years, and it costs much time and money. In addition, it cannot satisfy the need for real-time population data when we are faced with the emergency circumstances. With the development of high-altitude remote sensing technology, it has become an easy way to extract building information, to estimate population density, and to provide suggestions to the government for making decisions by employing the remote sensing images. It not only cost less money, but also cost a small amount of work. As a result, researches on population density are very meaningful.In this study, the author takes Beibei District of Chongqing as an example, uses high-resolution remote sensing images, and employs GIS spatial analysis and modeling technology, studies the study area’s population distribution in a normal day to the following aspects:1Get high-resolution remote sensing images from Google Earth, and research image correction and fusion.2. We conduct a study of the existing urban land use classification, and rise a urban land use classification system which is applicable to this study.3Conduct a study of spatial distribution and time distribution features of urban population.4Use visual identification, and build identify signs, identify the study area images.5Use binarization method to extract building shade, accord to graphic shape index and set area threshold to remove shadow noise of vegetation indices, use ArcGIS building Buffer Wizard to calculate the length of the shadow, and according to the shadow, the building height is calculated, and ultimately proposing building floors’ relationship with the shadow length.6Use improved land use density method, calculate each type of land use population density of each time period, and verify the model.7using ArcGIS Engine to develop a high-resolution urban population density simulation system, the systems can dynamic and in real-time display each block of land’s population density and population information. And take the study area occur a gas leak incident for example, demonstrate the system in city emergency application.The simulation results show that:the differences in spatial and temporal distribution of urban population distribution is very obvious, the land of industry, education, office and other public facilities have a larger population density in daytime, and residential land have a larger population density in night. The results indicate that:based on high-resolution remote sensing data, used GIS modeling technology, and improved land use density method have some theoretical significance and practical value for simulating high time and spatial resolution population density, and it has some significance for urban emergency, but we need to do further research to improve the accuracy.