Locating new commercial opportunities: Predict land use dynamics with the new metro line in Milan

In the previous study, by using the hierarchical agglomerative algorithm, 150000 commercial-activity-related POIs in the 200m*200m grid are classified into 16 patterns in Milan. We further trained a static prediction…

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WeSense: Make Places Greater Together – A crowdsourcing built environment data collection APP

https://www.youtube.com/watch?v=fmV13y5gAYk The living environment of JB Wei in Shanghai. From August to December in 2019, I used WeSense to record 71 days of my daily routes and totally collect more…

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Retail and Service Clusters: Commercial activities spatial pattern recognition with modified DBSCAN algorithm

This study aims to develop a generalizable approach to analyze the spatial distribution patterns of different types of retail and service businesses in a city. The analysis results provide business…

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Milan Collage: Milan land-use type identification and classification with clustering algorithms

This study aims to classify the land use types in the city with hierarchical agglomerative clustering algorithm. The study is part of my Master degree thesis at Politecnico di Milano…

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