- Python Basis GIS Functionalities
- Python Geocoding Addresses
- Python Nearest Neighbor Analysis
- Python Catchment Area Analysis
- Python GWR Data
- Python Raster Data
- Python PostgreSQL & PostGIS
- Python Tripadvisor
This section covers the basic Geographic Information System (GIS) functionalities in Python. It includes topics such as reading and writing spatial data, spatial data manipulation, and spatial data visualization.
Geocoding is the process of converting addresses into geographic coordinates. This section explains how to use the GeoAdmin API and Python libraries to geocode addresses, which can then be used for spatial analysis or mapping.
Nearest neighbor analysis is a technique to measure distances between spatial objects. This section covers how to perform nearest neighbor analysis in Python.
Catchment area analysis involves determining the area from which a particular location draws resources or customers. This section discusses how to perform catchment area analysis in Python, which can be useful in fields like retail location planning.
GWR (germ.: Gebäude- und Wohnungsregister) data is the most comprehensive dataset about buildings in Switzerland. It contains detailed information about the characteristics of buildings and their use. This section covers how to work with GWR data in Python, including how to read, manipulate, and vizualise the data to extract meaningful insights.
Raster data is a type of spatial data that represents a matrix of cells or pixels. This section discusses how to work with raster data in Python, including how to read, write, and manipulate raster data.
PostgreSQL is a powerful, open-source relational database system. PostGIS is a PostgreSQL extension that adds support for geographic objects, allowing location queries to be run in SQL. This section covers how to use Python to interact with PostgreSQL and PostGIS, including how to run spatial queries and how to read and write spatial data. The folder 'Docker_stuff' contains a docker-compose.yml and related files that can be used to run a Docker container locally (linux/amd64 only).
Extracting, analyzing, and visualizing spatial entities from Tripadvisor text data involves collecting user reviews and using Named Entity Recognition (NER) to extract locations. Geographic Information System (GIS) tools and mapping software are used to create visualizations like interactive maps and heatmaps, showcasing these locations. This process helps derive insights and make data-driven decisions based on spatial analysis.