دانلود کتاب یادگیری ماشین بر روی داده های جغرافیایی با استفاده از پایتون – معرفی ژئوداده با کاربردها و موارد استفاده

Spatial data has long been an ignored data type in general data science and statistics courses. Yet at the same time, there is a field of spatial analysis which is strongly developed. Due to differences in tools and approaches, the two fields have long developed in separate environments.

With the popularity of data in many business environments, the importance of treating spatial data is also increasing. The goal of the current book is to bridge the gap between data science and spatial analysis by covering tools of both worlds and showing how to use tools from both to answer use cases.

The book starts with a general introduction to geographical data, including data storage formats, data types, common tools and libraries in Python, and the like. Strong attention is paid to the specificities of spatial data, including coordinate systems and more.

The second part of the book covers a number of methods of the field of spatial analysis. All of this is done in Python. Even though Python is not the most common tool in spatial analysis, the ecosystem has taken large steps in user-friendliness and has great interoperability with machine learning libraries. Python with its rich ecosystem of libraries will be an important tool for spatial analysis in the near future.

The third part of the book covers multiple machine learning use cases on spatial data. In this part of the book, you see that tools from spatial analysis are combined with tools from machine learning and data science to realize more advanced use cases than would be possible in many spatial analysis tools. Specific considerations are needed for applying machine learning to spatial data, due to the specific nature of coordinates and other specific data formats of spatial data.