A Topological Algorithm for Determining How Road Networks Evolve Over Time
September 23, 2016 Β· Declared Dead Β· π SIGSPATIAL/GIS
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
M T Goodrich, Siddharth Gupta, Manuel R. Torres
arXiv ID
1609.07239
Category
cs.DS: Data Structures & Algorithms
Cross-listed
cs.CG
Citations
1
Venue
SIGSPATIAL/GIS
Last Checked
4 months ago
Abstract
We provide an efficient algorithm for determining how a road network has evolved over time, given two snapshot instances from different dates. To allow for such determinations across different databases and even against hand drawn maps, we take a strictly topological approach in this paper, so that we compare road networks based strictly on graph-theoretic properties. Given two road networks of same region from two different dates, our approach allows one to match road network portions that remain intact and also point out added or removed portions. We analyze our algorithm both theoretically, showing that it runs in polynomial time for non-degenerate road networks even though a related problem is NP-complete, and experimentally, using dated road networks from the TIGER/Line archive of the U.S. Census Bureau.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Data Structures & Algorithms
π
π
The Cartographer
R.I.P.
π»
Ghosted
Route Planning in Transportation Networks
R.I.P.
π»
Ghosted
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
R.I.P.
π»
Ghosted
Hierarchical Clustering: Objective Functions and Algorithms
R.I.P.
π»
Ghosted
Graph Isomorphism in Quasipolynomial Time
π
π
The Cartographer
Simulation optimization: A review of algorithms and applications
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted