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Geospatial & Temporal Data Resources

This page primarily contains a collection of links to different resources, that might come in handy in a modeling context where geospatial and temporal data needs to be collected, stored, analyzed and visualized. Other useful links and resources that are related to this topic will also be listed on this page.

Specialized Databases

Temporal data usually come in terms of time series i.e., sequences of data points indexed by time. In this case, if one wants to store temporal data in the most appropriate way, it might be a good idea to use a specialized database, that handle this type of data well. The most popular time series databases include:

Mapping Platforms

Several mapping platforms and services exist for creating interactive maps, which can be explored through a browser or a mobile application. Many of the mapping platforms, listed below, provide additional features for analysis of geospatial data, vehicle routing, travel time calculation, etc. These platforms are usually free to use up to a certian number of Application Programming Interface (API) calls.

  • Bing Maps (Free to some extent)

  • Deck (Free): An open-source project developed by Uber for geospatial data analysis

  • Google Maps (Free to some extent)

  • Here (Free to some extent)

  • Kepler (Free): An open-source project using MapBox for geospatial data analysis

  • Leaflet (Free). A python interface can be found on the folium github repository

  • MapBox (Free to some extent). A python interface can be found on the mapboxgl-jupyter github repository

  • OpenStreetMap (Free)

Geospatial Data Tools & Libraries

Data that have important geospatial charateristics sometimes need to be handled in a certain way, in order to maintain relations, attributes and e.g. properties such as distances between points, when data points are transformed (e.g. in the context of a map projection). Different data formats and tools have specifically been developed, to accomodate requirements of this type. A number of tools, that make working with geospatial data easier, is given below:

  • Geopandas: A python library that extends the pandas library to work with geospatial data types

  • JOSM: An editor that is specifically developed to work with OpenStreetMap data

  • OSMNX: A python library that makes it easy to download, extract, model and analyze traffic network data aquired from OpenStreetMap

  • QGIS: An editor for general mapping and Geographical Information System (GIS) tasks, that handles many kinds of geospatial data formats well

  • Utm: A python library that can convert between longtitude and latitude coordinates and coordinates given in the Universal Transverse Mercator (UTM) coordinate system

Web-based Data Visualization Libraries

A great way to visualize data is through an interactive web application. Two frameworks, that make the task of developing interactive web applications for data visualization, a lot easier, are among others:

  • Dash (Python & R): A Python and R framework developed

  • SandDance (Javascript): A javascript framework developed by Microsoft

The goal of both of these frameworks is to be able to easily set up a web application, such that a given data set can easily be presented, explored and understood.

Last edited: 16-10-2019