Prediksi Pertumbuhan Penduduk dengan Google Earth Pro Studi Kasus Kelurahan Madyopuro Kota Malang - Kuncoro Adi Pradono; Wibowo, Adi (Department Geography Education Faculty of Social Science - Universitas Negeri Jakarta, 2024)
The construction of the Trans Java toll road has brought development to the areas it passes through. As happened in Madyopuro Village, Malang City, which was transformed into a satellite city due to the construction of the toll exit, some unoccupied land in this area began to be looked at by developers to build settlements known from the 2019 image. In addition, supporting facilities in the form of arterial roads were widened so that access to the eastern region of the village became easy. This settlement development factor is an indicator of population growth. The purpose of this research is to analyze changes in the number of settlements with Google Earth Data. The method used is snapshoot analysis through Google Earth Pro image data which is available in full and easily accessible in a multi-spatial and multi-temporal manner. The results are used to predict the amount of population growth. Images from Google Earth are rectified then the amount of built-up land in the form of settlements is calculated through the appearance of the roof. Furthermore, the number of each building is correlated with the population of each building based on SNI-03-1733-2004. The results of this calculation were compared with population data for that year using data from the Central Statistics Agency (BPS). The results of this research show that the population based on predictions in 2015 amounted to 18,604 people, in BPS data 19,566 people. For 2019, predictions with this method show a number of 20,020 people and BPS data shows 20,067 people. The prediction in 2023 is 21,544 people. Through image data from Google Earth Pro, the population predicted by this method is close to data from BPS in 2015 and 2019 with a difference. The conclusion is that data on changes in house buildings from Google Earth can be used to predict the number of people affected by toll road construction.