Geovisualization of Spatio-temporal data combined with self-reported data is a very promising keynote, especially for social sciences. Space-time analysis combined with mobile data may reveal numerous latent information about human behaviors and It is possible to use a space-time cube to divulge activity, travel patterns of different genders, racial groups, non-employees, etc., and all this information can be used in broad range of domains including urban planning, transportation, location-based services, mobility optimization, sports analytics, and behavioral studies and many other purposes.In this work, I used the GPS data that describe the movements of 3 subjects and Time-diary self-report data. The data was collected for 13-days from 3 subjects through the iLog-mobile application.

• The specific objectives of this project are:

  • To visualize 2D and 3D visual narratives of the 3 subjects
  • To visualize 3D Space Time Cube from a given GPS track of the 3 subjects
  • To visualize 4D (Space Time Cube + Time-diaray data[mood,activity])
  • Mapping the person in the society, Telling the 3-weeks story of the subject, geography with analysis of behavior, and integrating ideas in human geography with human behavior.

The project was done for the completion of STUDIES ON HUMAN BEHAVIOUR course at the University of Trento.The report paper can be found here

• The map provides contextual information and makes it possible to understand subject trajectory

• Space-time cube is one of the most efficient 3D geo-visualization techniques that contribute the Spatio-temporal data comprehension of human behavior[1].
• This concept was first introduced by Hägerstrand-Swedish geographer. According to Hägerstrand; a life path can be visualized as a 3D space by projecting it on a 2D plane. As the base (x and y-axis) corresponds the geography, the height (z-axis) represents time. [2]
• There are different libraries that can be used for plotting data in 3D, but I used Plotly library which provides a relatively easy and straightforward API to generate 3D interactive visualizations and mplot3d for non-interactive 3D-viz

• Run the Subject_1_2D-3D_STC_VIZ.ipynb notebook for intractive/dynamic visualization of the 3D image

• Run the Subject_1_2D-3D_STC_VIZ.ipynb notebook for intractive visualization

• Run the Subject_3_2D-3D_STC_VIZ.ipynb notebook for intractive/dynamic visualization of the 3D image

• Run the Subject_1_2D-3D_STC_VIZ.ipynb notebook for intractive/dynamic visualization of the 3D image

• Run the Subject_3_2D-3D_STC_VIZ.ipynb notebook for intractive visualization

• Given spcfic hour of the day find the frequency of activity

• Given spcfic activity what is the mood of the subject

• The line looks stright in the z-axis because we have only one location /co-ordinate information

• The line looks stright in the z-axis because we have only one location /co-ordinate information

Here is the Link for the Two datasets.

The ptoject uses:

• Python v3+
• Numpy v1.20.3
• Pandas v1.3.4
• Geopandas v0.10.2
• Plotly v5.4.0

Before executing the notebooks the following dependencies must be installed in your Python's environment

!conda install --channel conda-forge cartopy 
!pip install pandas keplergl movingpandas 
!pip install topojson 
!pip install geojson
!pip install -U kaleido
!pip install scikit-image

or you can Create your conda environment and install dependencies from the requirements.txt file accordingly

conda create -n "enviroment-name" python=3.6

and install the dependancies.

pip install -r requirment.txt

• README.md: file containing all the relevant information to run the project.
• requirements: file containing all the necessary libraries to install.
• GPS_data_EDA.ipynb: python notebook to perform the preprocessing of the entire GPS data and to extract data of each subject.
• Time diary-data-EDA.ipynb: python notebook to perform the preprocessing of the entire Time-diary data and to extract data of each subject.
• Subject_1_2D-3D_STC_VIZ.ipynb: python notebook used to visualize the 2D and 3D maps and GPS data distribution of each subject_1/ same for other subjects.
• Subject_1_time_diary_dataviz.ipynb: python notebook used to visualize the 2D and 4D maps and Time-diary data distribution of subject_1/ same for other subjects.
• all_subject_dataviz.ipynb: python notebook used to visualize and compare the 3-subjects Time-diary data.

After installing all dependencies in your Python's environment, execute the desired script using below commands.There are different ways to run the notebook. Here simpler way have been presented.

• Step 1: Dowenload and extract the dataset from above provided link
• Step 2: Change the working directory of the project to the project folder
• Step 3: run the following command on the cmd

jupyter notebook

• Step 4: A browser will pop up with a notebook. If the browser is not press a link on the cmd which has "htts://localhost:" in it. This will open a notebook on a browser.

• Step 5: First run the GPS_data_EDA.ipynb notebook to get processed GPS data of each subject before running any other notebook. After this step, you can run all the 3d visualization of each subject by only changing the path of the directory in which you saved the processed data of each subject.

• Step 5: First run the Time diary-data-EDA.ipynb notebook to get processed time diary data of each subject before running any other notebook. After this step, you can open and run the time diary notebooks ro see the 2D and 4D visualization of each subject by only changing the path of the directory in which you saved the processed time diary data of each subject.

• Step 7 : Now you can open any notebook and each cell can be run by pressing "SHIFT + ENTER" on your machine

1. Kraak, M. J. (2003, August). The space-time cube revisited from a geovisualization perspective. In Proc. 21st International Cartographic Conference (pp. 1988-1996). Citeseer.
2. Kraak, M. J. (2008). Geovisualization and time–new opportunities for the space-time cube. Geographic visualization: concepts, tools, and applications, 293-306.