Urban structure accessibility modeling and visualization for joint spatiotemporal constraints
In modern cities, service providers want to identify the regions that are hard to reach from
multiple fire stations, a citizen wants to meet with friends in a restaurant close to everyone …
multiple fire stations, a citizen wants to meet with friends in a restaurant close to everyone …
Towards robust representations of spatial networks using graph neural networks
The effectiveness of a machine learning model is impacted by the data representation used.
Consequently, it is crucial to investigate robust representations for efficient machine learning …
Consequently, it is crucial to investigate robust representations for efficient machine learning …
[HTML][HTML] Develo** a multi-classifier system to classify OSM tags based on centrality parameters
Misclassification of features is a major source of uncertainty in OpenStreetMap (OSM). This
study is an automated data-enrichment study whose primary goal is predicting road classes …
study is an automated data-enrichment study whose primary goal is predicting road classes …
Rapid configurational analysis using OSM data: towards the use of Space Syntax to orient post-disaster decision making
This paper addresses the problem of the growing exposure of contemporary cities to natural
hazards by discussing the theoretical, methodological and practical aspects of using the …
hazards by discussing the theoretical, methodological and practical aspects of using the …
Heuristics for k-domination models of facility location problems in street networks
P Corcoran, A Gagarin - Computers & Operations Research, 2021 - Elsevier
We present new greedy and beam search heuristic methods to find small-size k-dominating
sets in graphs. The methods are inspired by a new problem formulation which explicitly …
sets in graphs. The methods are inspired by a new problem formulation which explicitly …
Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data
OpenStreetMap (OSM) produces a huge amount of labeled spatial data, but its quality has
always been a deep concern. Numerous quality issues have been discussed in the vast …
always been a deep concern. Numerous quality issues have been discussed in the vast …
Improved graph neural networks for spatial networks using structure-aware sampling
Graph Neural Networks (GNNs) have received wide acclaim in recent times due to their
performance on inference tasks for unstructured data. Typically, GNNs operate by exploiting …
performance on inference tasks for unstructured data. Typically, GNNs operate by exploiting …
Extending QGIS processing toolbox for assessing the geometrical properties of OpenStreetMap data
OpenStreetMap (OSM) offers an under-explored crowdsourced geospatial data useful to
urban street network researchers for assessing the geometrical properties of spatial data …
urban street network researchers for assessing the geometrical properties of spatial data …
Incorporating ideas of structure and meaning in interactive multi scale map** environments
Web based, slippy, scalable maps are common place. Interacting with such digital maps at
varying levels of detail is key to interpretation, and exploration of different geographies. The …
varying levels of detail is key to interpretation, and exploration of different geographies. The …
Transferable graph neural networks for inferring road type attributes in street networks
In this paper, we study transferable graph neural networks for street networks. The use of
Graph Neural Networks in a transfer learning setting is a promising approach to overcome …
Graph Neural Networks in a transfer learning setting is a promising approach to overcome …