• Stars
    star
    9
  • Rank 1,939,727 (Top 39 %)
  • Language
    Jupyter Notebook
  • License
    MIT License
  • Created about 2 years ago
  • Updated over 1 year ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Benchmarking of spatial regression methods with respect to spatial heterogeneity, and providing a Python implementation of spatial Random Forests

More Repositories

1

trackintel

trackintel is a framework for spatio-temporal analysis of movement trajectory and mobility data.
Python
204
star
2

location-prediction

[TRC] Context-aware next location prediction
Python
29
star
3

location-mode-prediction

[SIGSPATIAL '22] Next location prediction considering travel mode
Python
14
star
4

Graph-based-mobility-profiling

Code accompanying our paper "Graph-based Mobility Profiling"
Python
7
star
5

traffic4cast

Submission to the iarai traffic4cast competition
Python
6
star
6

traffic4cast-Graph-ResNet

Python
5
star
7

V2G-carsharing-RL-environment

V2G for car sharing thesis project
Jupyter Notebook
4
star
8

mode_detect

[JTRG] Geospatial context importance for travel mode detection
Jupyter Notebook
4
star
9

trip_purpose_privacy

Understanding the predictability of activity purposes
Python
3
star
10

bike_lane_optimization

Python
3
star
11

change-detection

[GIScience '21] Travel behaviour change detection study
Python
3
star
12

reprotrack

SCSS
1
star
13

rooftop-PV-EV-charging

Python
1
star
14

trackintel-docs

The trackintel documentation, please visit:
HTML
1
star
15

Complexity-Aware-Traffic-Prediction

HTML
1
star
16

topology_privacy

How privacy-preserving are graph representations of mobiltiy
Python
1
star
17

train_delay

Python
1
star
18

graph-trackintel

Tools for building and preprocessing location graphs
Python
1
star
19

v2g4carsharing

Vehicle-to-grid strategies for car sharing systems
Python
1
star
20

ev_homepv

A project to analyze how much of our mobility energy demand (induced by electric vehicles) we can cover by using only pv installed on our own home.
Python
1
star
21

geospatial_optimal_transport

Python
1
star