Conflating point of interest (POI) data: A systematic review of matching methods

Sun, Kai; Hu, Yingjie; Ma, Yue; Zhou, Zhenqi; Zhu, Yunqiang

doi:10.1016/j.compenvurbsys.2023.101977

Cited by 7 publications

(1 citation statement)

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“…Identifying nearest neighbours typically involves estimating their distances from the subject point of interest (POI). This computational challenge is commonly addressed using a range-query-oriented spatial index like KD-Tree, R-Tree, or Ball-Tree, which may be efficient for full-distance matrix computation [58]. However, a predetermined boundary is often set in urban planning issues such as housing density estimation, a radial buffer or a census tract/statistical area unit are examples of the boundary used.…”

Section: Methodsmentioning

confidence: 99%

Disentangling the Modifiable Areal Unit Problem in Housing Density and Price Associations

Cheung,

Sham,

Yiu

2024

Buildings

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Urban planning education must address the Modifiable Areal Unit Problem (MAUP) to comprehend the critical impact of urban density on sustainable city development. Quantitative studies using administrative area units face indefinite aggregate level biases. This paper introduces an efficient block-searching method to calculate property densities around residences of various boundary scales and empirically examines their relationship with housing prices in Auckland, New Zealand. Results reveal negative associations between housing prices and densities within neighbourhoods, emphasising the limitations of administrative boundaries. These findings underscore the necessity for planning education to navigate MAUP’s complexities in shaping urban development policies.

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Section: Methodsmentioning

confidence: 99%