Generating population census data through aerial remote sensing

1994 ◽  
Vol 22 (3) ◽  
pp. 131-138 ◽  
Author(s):  
R. C. S. Taragi ◽  
K. S. Bisht ◽  
B. S. Sokhi
Keyword(s):  
Remote Sensing ◽  
Census Data ◽  
Population Census ◽  
Aerial Remote Sensing

scholarly journals Using remote sensing data in population density estimation

2021 ◽  
Author(s):  
E.G. Shvetsov ◽  
N.M. Tchebakova ◽  
E.I. Parfenova
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Population Density ◽  
Vegetation Index ◽  
Census Data ◽  
Remote Sensing Data ◽  
Satellite System ◽  
Population Census ◽  
Krasnoyarsk Krai ◽  
Using Data

In recent decades, remote sensing methods have often been used to estimate population density, especially using data on nighttime illumination. Information about the spatial distribution of the population is important for understanding the dynamics of cities and analyzing various socio-economic, environmental and political factors. In this work, we have formed layers of the nighttime light index, surface temperature and vegetation index according to the SNPP/VIIRS satellite system for the territory of the central and southern regions of the Krasnoyarsk krai. Using these data, we have calculated VTLPI (vegetation temperature light population index) for the year 2013. The obtained values of the VTLPI calculated for a number of settlements of the Krasnoyarsk krai were compared with the results of the population census conducted in 2010. In total, we used census data for 40 settlements. Analysis of the data showed that the relationship between the value of the VTLPI index and the population density in the Krasnoyarsk krai can be adequately fitted (R 2 = 0.65) using a linear function. In this case, the value of the root-meansquare error was 345, and the relative error was 0.09. Using the obtained model equation and the spatial distribution of the VTLPI index using GIS tools, the distribution of the population over the study area was estimated with a spatial resolution of 500 meters. According to the obtained model and the VTLPI index, the average urban population density in the study area exceeded 500 people/km2 . Comparison of the obtained data on the total population in the study area showed that the estimate based on the VTLPI index is about 21% higher than the actual census data.

scholarly journals Population Spatialization in Beijing City Based on Machine Learning and Multisource Remote Sensing Data

2020 ◽  
Vol 12 (12) ◽  
pp. 1910 ◽  
Author(s):  
Miao He ◽  
Yongming Xu ◽  
Ning Li
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Census Data ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Google Earth ◽  
Population Census ◽  
Beijing City ◽  
Night Time ◽  
Sensing Data

Remote sensing data have been widely used in research on population spatialization. Previous studies have generally divided study areas into several sub-areas with similar features by artificial or clustering algorithms and then developed models for these sub-areas separately using statistical methods. These approaches have drawbacks due to their subjectivity and uncertainty. In this paper, we present a study of population spatialization in Beijing City, China based on multisource remote sensing data and town-level population census data. Six predictive algorithms were compared for estimating population using the spatial variables derived from The National Polar-Orbiting Partnership/ Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) night-time light and other remote sensing data. Random forest achieved the highest accuracy and therefore was employed for population spatialization. Feature selection was performed to determine the optimal variable combinations for population modeling by random forest. Cross-validation results indicated that the developed model achieved a mean absolute error (MAE) of 2129.52 people/km2 and a R2 of 0.63. The gridded population density in Beijing at a spatial resolution of 500 m produced by the random forest model was also adjusted to be consistent with the census population at the town scale. By comparison with Google Earth high-resolution images, the remotely-sensed population was qualitatively validated at the intra-town scale. Validation results indicated that remotely sensed results can effectively depict the spatial distribution of population within town-level districts. This study provides a valuable reference for urban planning, public health and disaster prevention in Beijing, and a reference for population mapping in other cities.

Land development, land use, and urban sprawl in Puerto Rico integrating remote sensing and population census data

2007 ◽  
Vol 79 (3-4) ◽  
pp. 288-297 ◽  
Author(s):  
Sebastián Martinuzzi ◽  
William A. Gould ◽  
Olga M. Ramos González
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Puerto Rico ◽  
Urban Sprawl ◽  
Census Data ◽  
Land Development ◽  
Population Census

Machine learning-based crop recognition from aerial remote sensing imagery

2021 ◽  
Vol 15 (1) ◽  
pp. 54-69
Author(s):  
Yanqin Tian ◽  
Chenghai Yang ◽  
Wenjiang Huang ◽  
Jia Tang ◽  
Xingrong Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Remote Sensing Imagery ◽  
Aerial Remote Sensing

PERANCANGAN APLIKASI SENSUS PENDUDUK PADA BADAN PUSAT STATISTIK KOTA MAKASSAR BERBASIS ANDROID

2021 ◽  
Vol 6 (1) ◽  
pp. 48
Author(s):  
Nurkhalik Wahdanial Asbara
Keyword(s):  
Data Collection ◽  
Census Data ◽  
Population Data ◽  
Accurate Information ◽  
Population Census ◽  
Male Population ◽  
Female Population ◽  
Technological Developments ◽  
The Government ◽  
Population Ratio

Technological developments and changes in government systems are developing rapidly. Both of these lead to efforts to carry out duties, protect functions and serve the community. This encourages the government to take various adjustment steps quickly in line with the dynamics of development that occur. One of them is through a population census. The population census is an important issue that must be handled properly. The population census in this study takes population data in an area based on the number of male population, female population, ratio, and population density. The data was taken and submitted to the Makassar City Statistics Agency. Population Census is a presentation of information that has the ability to present accurate information, and helps facilitate the search for a population census data. The population census is carried out every 5 years which is carried out by census officers to carry out data collection to each resident's house, the data collection process is carried out by conventional recording and submitting it to the central statistics agency for database entry. With this application, it is expected to provide convenience to Population census officers to perform the process of inputting population data and the data is directly stored in the database without having to return to the office to input again.

The State of Development in Villages of India: An Analysis of the 2011 Census Data

2018 ◽  
Vol 12 (3) ◽  
pp. 305-325 ◽  
Author(s):  
Aalok Ranjan Chaurasia
Keyword(s):  
Present Article ◽  
Small Proportion ◽  
Census Data ◽  
The State ◽  
Programming Approach ◽  
Population Census ◽  
Development Index ◽  
Village People ◽  
Development Approach ◽  
The Village

The present article uses data available through the 2011 population census to analyze the state of development in the villages of India on the basis of a village development index that has been constructed for the purpose following the capabilities expansion as development approach. The analysis reveals that the state of development in the villages of the country varies widely and there is only a small proportion of the villages where the state of development can be termed as satisfactory. The analysis also reveals that the state of development in the village is influenced by its selected defining characteristics. The article calls for a village-based planning and programming approach for meeting the development and welfare needs of the village people.

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