Introduction to Geospatial Data and Python Programming
D. Shanmugapriyaa, M. Sujithra, B. Senthilkumar, V. Sachin Kumar, Ram Sanjai · Advances in geospatial technologies book series · 2024
Utilising geographical linkages, predictive modelling, and problem-solving techniques, geospatial analysis is an essential discipline in many businesses. This chapter explores the deep meaning of geographical data and how machine learning can benefit from it. Geospatial data, derived from sources such as GPS devices and satellite pictures, serves as the basis for comprehending infrastructure, topography, weather, and population dynamics. Crowdsourced information and open data projects enhance the data set available for research. By addressing issues like dimensionality reduction and missing data, spatial data preparation ensures the quality and diversity of data sources. Geospatial analysis is improved for a variety of applications using machine learning algorithms, which include supervised, unsupervised, and reinforcement learning techniques. Temporal dynamics, as examined by methods such as ARIMA and LSTM networks, track variations within regions. Ethical considerations, efficient data visualization, and data fusion techniques all contribute to thorough.