Abstract
A common approach to evaluating the quality of urban environments in terms of walkability is to measure the accessibility of walking attractors. For this purpose, the information on street network configuration and distribution of walking attractors is required. However, in the early planning stages when not all the necessary data on land use allocation is available, or if the knowledge about the walkability impact of the pure street network configuration is required, this approach is of little use. By addressing this deficiency, we developed method for predicting the accessibility of walking attractors only by using the information on the street network configuration. This method is based on the hypothesis that street network configuration influences how people move through space, and this in turn affects the allocation and accessibility of walking attractors. We empirically test this hypothesis in a case study of Weimar, Germany and found that street network configuration alone was significant and the strongest predictor of AWA. We show how street network influences the distribution of people in terms of pedestrian movement flows and that the access to these movement flows is highly correlated to the neighbourhood walkability. This highlights the importance of urban structure as an interface for social interaction and suggests the positive effect of social proximity on the quality of environment.
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Notes
Walking attractors are considered on the level of an individual or are aggregated on a group of people/population
Locations of specific functions and land uses could be automatically acquired from open geo databases such as Google Maps, Education.com, Open Street Map, the US Census and Localeze
The aggregation illustrates that the accessibility is not measured between specific individuals but as an aggregation to the whole population represented in terms of “movement flows”
The movement network considered in Space Syntax analysis is an extension of the street network. All accessible public spaces are connected through movement network including special multi-level connections such as tunnels, skywalk etc.
The movement network is considered as a two-dimensional projection with no information about the slope of a street segment. This may reduce the predictive power of the model in urban structures with high variation in elevation.
A distance decay parameter β of 0.00217 in meters correspond to 0.1813 in temporal units as defined by Handy and Niemeier (1997).
In the analysis of street networks, the ‘edge effect’ describes a bias in the analysis results as a product of portion of the network included in the analysis – the edge (Okabe and Sugihara 2012). Different measures have different degrees of sensitivity towards the ‘edge effect’, mostly depending on the radius of the analysis (Gil 2015). In the case study presented in this paper, we avoid the ‘edge effect’ by analyzing the whole city of Weimar. Since no additional settlements were found within the boundary of the maximum analysis radius (2000 m) from the edge of the city, there would be no change in analysis results if the edge were extended.
Walk Score recognises eight types of walking attractors: Errands, Culture, Grocery, Park, Dining and Drinking, School, Shopping (walkscore.com). Their individual weighting was empirically calibrated based on their contribution to moderate and vigorous physical activity (Frank 2013).
The resolution of Walk Score is on the level of the individual house. Walk Score can be assessed for any location worldwide, however location outside the US, Canada, Australia and New Zealand should be additionally validated, since the geo-located data is not always complete (walkscore.com).
Whitepaper describing the functionality of the DecodingSpaces toolbox is available at: https://e-pub.uni-weimar.de/opus4/frontdoor/index/index/docId/2738
R Core Team (2013).
In recent year several pseudo R-square measures has been developed, but their applicability is limited and therefore harder to interpret compared to the linear regression model.
The contribution of any destination above 600 m to the overall accessibility with the distance decay parameter β of 0.00217 is only 27%.
We modeled the curvilinear relationship between ASA and AWA by fitting the polynomial regression function. The best fit maintaining all regression parameters significant was achieved by 3rd degree polynomial function.
For spatial interpolation we use the “automap” package by Paul Hiemstra for statistical software R. This package performs an automatic interpolation by automatically estimating the variogram.
Based on the Walk Score classification is 26% of street segments in Weimar fully car dependent (Walk Score < 25).
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Acknowledgements
This study was carried out as part of the research project ESUM - Analysing trade-offs between the energy and social performance of urban morphologies funded by the German Research Foundation (DFG) and Swiss National Science Foundation (SNSF).
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Appendices
Appendix 1
1.1 ASA as Predictor of AWA (Walk Score) – Residual Analysis
To validate the predictive linear regression model of Walk Score based on the ASA we conduct the regression residual analysis. First we examine the residual variance (Fig. 10a). We found uniform distribution of residual variance with decrease at maximum Walk Score values. This could be accounted for the fact that Walk Score index as opposed to the ASA has introduced artificial cut-off value at 100 points. As next we test the normality of error terms (Fig. 10b) and observe that the residuals follow the normal distribution. Finally, the leverage plot (Fig. 10c) doesn’t indicate any potential measurement errors and their influence on the regression model. We conclude that the residual analysis didn’t reveal any systematic patterns indicating errors in the predictive model.
Residual plots: a Residuals vs. Fitted values, b QQ plot and c Residuals vs. Leverage
Appendix 2
1.1 Pedestrian Movement Flows vs. Access to Pedestrian Movement Flows
The relation between predicted movement flows and the access to these movement flows show the high collinearity of both measures (Pearson’s correlation coefficient R = .67, p value ≤ .001, see Fig. 10b). However, we observe discrepancy at locations with a rapid drop of movement between neighboring street segments. While this high deviation in movement flows among geographically close locations is a common phenomenon in street networks, physical access to an opportunity doesn’t change abruptly every few steps (Fig. 11a). With this in mind, we can consider the ASA as smoothing function of movement potential.
a Movement potential (Betweenness centrality R600m) mapped onto the street network (Black = High Betweenness, Light grey = Low Betweenness) and access to movement potential (ASA) mapped onto buildings (Red = High ASA, Blue = Low ASA). b A scatter plot showing the relationship between ASA and betweenness centrality R600m
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Bielik, M., König, R., Schneider, S. et al. Measuring the Impact of Street Network Configuration on the Accessibility to People and Walking Attractors. Netw Spat Econ 18, 657–676 (2018). https://doi.org/10.1007/s11067-018-9426-x
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DOI: https://doi.org/10.1007/s11067-018-9426-x