Geoff BoeingHow many street intersections do you see in this figure? I published an article recently in Transactions in GIS (open-access: https://doi.org/10.1111/tgis.70037) and its first sentence sums it up: "Counting is hard." Hear me out... It really is!
Most real-world objects belong to fuzzy categories, resulting in subjective decisions about what to include or exclude from counts. Yet this complexity is often obscured by a superficial impression that counting is easy to do because its mechanics seem easy to understand.
But counting is *hard* because defining that set and identifying its members are often nontrivial tasks. Many of the world’s most important analytics rely far less on flashy data science techniques than they do on counting things well and justifying those counts effectively.
Street intersection counts and densities are ubiquitous measures in transport geography and planning. However, typical street network data and typical street network analysis tools can substantially overcount them. This article explains the 3 main reasons why this happens and presents solutions.
Street intersections, particularly the complex kind common in modern car-centric urban areas, are fuzzy objects for which most data sources do not provide a simple 1:1 representation.
This causes spatial uncertainty due to data challenges in representing network nonplanarity, intersection complexity, and curve digitization. Essentially all data sources suffer from at least 1 of these problems in representing divided roads, slip lanes, roundabouts, interchanges, turning lanes, etc
If unaddressed, my assessment shows that typical intersection counts (and downstream densities) would be overestimated by >14%, but very unevenly so in different parts of the world. This bias’s extreme heterogeneity particularly hinders comparative urban analytics.
Mitigating these 3 problems is a project I’ve been iteratively refining for the past decade. It was a central focus of my dissertation and a key motivation for originally developing OSMnx.
This article presents OSMnx’s algorithms to automatically simplify spatial graphs of urban street networks—via edge simplification and node consolidation—resulting in faster parsimonious models and more accurate network measures like intersection counts and densities, street lengths, & node degrees.
These algorithms’ information compression drastically improves downstream graph analytics’ memory and runtime efficiency, boosting analytical tractability without loss of model fidelity.
Counting is hard, but we can make it a little easier by using better models. For more, check out the open-access article: https://doi.org/10.1111/tgis.70037
ktyl
Thijs Lucas@systemagie darüber hattest Du auch mal berichtet. Vielleicht ja interessant für dich.
Holger Bruch@thijs_lucas ja, neben der Unfall-Cluster-Analyse mittels DBSCAN habe ich #osmnx genutzt, um Unfallorte Kreuzungen zuzuordnen. Und aktuell für ein Projekt, um Fußwege an Bahnhöfen und deren Barrieren zu ermitteln. Großartiges Projekt!