A utility-based spatial analysis of residential street-level conditions; A case study of Rotterdam

• URL: http://arxiv.org/abs/2410.17880v1
• Date: Wed, 23 Oct 2024 13:52:22 GMT
• Title: A utility-based spatial analysis of residential street-level conditions; A case study of Rotterdam
• Authors: Sander van Cranenburgh, Francisco Garrido-Valenzuela,
• Abstract summary: This paper investigates the spatial distribution of utility derived from street-level conditions in residential location choices on a city-wide scale. We find that the utility derived from street-level conditions varies significantly on a highly localised scale. Our results also reveal that the high real-estate prices in the city centre cannot be attributed to attractive street-level conditions.
• Score: 1.2289361708127877
• License:
• Abstract: Residential location choices are traditionally modelled using factors related to accessibility and socioeconomic environments, neglecting the importance of local street-level conditions. Arguably, this neglect is due to data practices. Today, however, street-level images -- which are highly effective at encoding street-level conditions -- are widely available. Additionally, recent advances in discrete choice models incorporating computer vision capabilities offer opportunities to integrate street-level conditions into residential location choice analysis. This study leverages these developments to investigate the spatial distribution of utility derived from street-level conditions in residential location choices on a city-wide scale. In our case study of Rotterdam, the Netherlands, we find that the utility derived from street-level conditions varies significantly on a highly localised scale, with conditions rapidly changing even within neighbourhoods. Our results also reveal that the high real-estate prices in the city centre cannot be attributed to attractive street-level conditions. Furthermore, whereas the city centre is characterised by relatively unattractive residential street-level conditions, neighbourhoods in the southern part of the city -- often perceived as problematic -- exhibit surprisingly appealing street-level environments. The methodological contribution of this paper is that it advances the discrete choice models incorporating computer vision capabilities by introducing a semantic regularisation layer to the model. Thereby, it adds explainability and eliminates the need for a separate pipeline to extract information from images, streamlining the analysis. As such, this paper's findings and methodological advancements pave the way for further studies to explore integrating street-level conditions in urban planning.

Related papers

• Eyes on the Streets: Leveraging Street-Level Imaging to Model Urban Crime Dynamics [0.0]
  This study addresses the challenge of urban safety in New York City by examining the relationship between the built environment and crime rates. We aim to identify how urban landscapes correlate with crime statistics, focusing on the characteristics of street views and their association with crime rates.
  arXiv  Detail & Related papers  (2024-04-15T21:33:45Z)
• Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks [82.82866901799565]
  We build a new set of multimodal remote sensing benchmark datasets (including hyperspectral, multispectral, SAR) for the study purpose of the cross-city semantic segmentation task. Beyond the single city, we propose a high-resolution domain adaptation network, HighDAN, to promote the AI model's generalization ability from the multi-city environments. HighDAN is capable of retaining the spatially topological structure of the studied urban scene well in a parallel high-to-low resolution fusion fashion.
  arXiv  Detail & Related papers  (2023-09-26T23:55:39Z)
• A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection [68.84486900183853]
  We build an urban region graph (URG) to model the urban area in a hierarchically structured way. Then, we design a novel contextual master-slave framework to effectively detect the urban village from the URG. The proposed framework can learn to balance the generality and specificity for UV detection in an urban area.
  arXiv  Detail & Related papers  (2022-11-26T18:17:39Z)
• City-Wide Perceptions of Neighbourhood Quality using Street View Images [5.340189314359048]
  This paper describes our methodology, based in London, including collection of images and ratings, web development, model training and mapping. Perceived neighbourhood quality is a core component of urban vitality, influencing social cohesion, sense of community, safety, activity and mental health of residents.
  arXiv  Detail & Related papers  (2022-11-22T10:16:35Z)
• Urban form and COVID-19 cases and deaths in Greater London: an urban morphometric approach [63.29165619502806]
  The COVID-19 pandemic generated a considerable debate in relation to urban density. This is an old debate, originated in mid 19th century's England with the emergence of public health and urban planning disciplines. We describe urban form at individual building level and then aggregate information for official neighbourhoods.
  arXiv  Detail & Related papers  (2022-10-16T10:01:10Z)
• Visual Perception of Building and Household Vulnerability from Streets [0.294944680995069]
  In developing countries, building codes often are outdated or not enforced. A large portion of the housing stock is substandard and vulnerable to natural hazards and climate related events. We propose an evaluation framework that is cost-efficient for first capture and future updates.
  arXiv  Detail & Related papers  (2022-05-28T15:35:47Z)
• CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials [6.573006589628846]
  Most cities lack a spatial catalog of their surfaces due to the cost-prohibitive and time-consuming nature of data collection. Recent advancements in computer vision, together with the availability of street-level images, provide new opportunities for cities to extract large-scale built environment data. We propose CitySurfaces, an active learning-based framework that leverages computer vision techniques for classifying sidewalk materials.
  arXiv  Detail & Related papers  (2022-01-06T21:58:37Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)
• Local Betweenness Centrality Analysis of 30 European Cities [0.0]
  We show that road segments important for long-distance transportations are concentrated along larger streets while those for short range transportations form clusters around CBD, historical, or residential districts. Our findings are useful for urban planners and decision-makers to understand the current situation of the city and make informed decisions.
  arXiv  Detail & Related papers  (2021-03-21T17:08:04Z)
• Predicting Livelihood Indicators from Community-Generated Street-Level Imagery [70.5081240396352]
  We propose an inexpensive, scalable, and interpretable approach to predict key livelihood indicators from public crowd-sourced street-level imagery. By comparing our results against ground data collected in nationally-representative household surveys, we demonstrate the performance of our approach in accurately predicting indicators of poverty, population, and health.
  arXiv  Detail & Related papers  (2020-06-15T18:12:12Z)
• Entropy as a measure of attractiveness and socioeconomic complexity in Rio de Janeiro metropolitan area [52.77024349608834]
  We use a mobile phone dataset and an entropy-based metric to measure the attractiveness of a location. The results show that the attractiveness of a given location measured by entropy is an important descriptor of the socioeconomic status of the location.
  arXiv  Detail & Related papers  (2020-03-23T15:58:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.