Statistical modelling and analysis of big data on pedestrian movement
Paper i proceeding, 2019
This paper aims therefore, to directly address three methodological challenges: first, construction of comparable GIS-models; second, gathering large scale pedestrian data; third, applying advanced statistical modelling suitable for pedestrian data.The ultimate goal is to estimate the impact of spatial form on urban life in a way that is methodologically sound and can provide robust results that can be generalisable, and allows us to speak of the relation between spatial form and pedestrian movement in a way that is not specific to a certain area, or types of areas or streets, or even to a specific city.
The results show, first, high and consistent correlations between spatial form and pedestrian movement in a study of unprecedented size that comprises three cities, including a large range of neighbourhoods of varying morphological types, from villa areas to urban cores, and offer convincing proof that the tested morphological variables have a strong impact on the spatial distribution of pedestrian flows in cities. Second, the study shows that the model with all explanatory variables has the highest explanatory power and the best model fit where Angular integration and Accessible FSI are the explanatory variables with the largest effect on pedestrian movement, but others are significantly contributing to the predictive power of the model. Third, the study contributes to the advancement of the statistical modelling that is suitable for the specificities of the data used, proposing the use of a negative Binomial model instead of regression models, most common in the field.
spatial morphology
statistical modelling
anonymised pedestrian survey
pedestrian movement
spatial analysis
Författare
Ioanna Stavroulaki
Chalmers, Arkitektur och samhällsbyggnadsteknik, Stadsbyggnad
David Bolin
Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik
Meta Berghauser Pont
Chalmers, Arkitektur och samhällsbyggnadsteknik, Stadsbyggnad
Lars Marcus
Chalmers, Arkitektur och samhällsbyggnadsteknik, Stadsbyggnad
Erik Håkansson
Chalmers, Matematiska vetenskaper, Algebra och geometri
12th International Space Syntax Symposium, SSS 2019
79
Beijing, China,
Spatial Morphology Lab _ SMoL
Chalmers, 2015-02-01 -- 2017-11-30.
Ämneskategorier
Arkitekturteknik
Infrastrukturteknik
Arkitektur
Sannolikhetsteori och statistik
Drivkrafter
Hållbar utveckling
Styrkeområden
Transport
Building Futures (2010-2018)
Relaterade dataset
Spatial Morphology Lab 01. International laboratory for comparative research in urban form. Street networks, Sweden - Non-Motorised network of Stockholm [dataset]
DOI: 10.5878/hfww-5y22