Land-use dynamic discovery based on heterogeneous mobility sources

被引：5

作者：

Terroso-Saenz, Fernando ^{[1
]}

Munoz, Andres ^{[1
]}

Arcas, Francisco ^{[1
]}

机构：

[1] Univ Catolica San Antonio Murcia UCAM, Polytech Sch, Campus Jeronimos, Murcia 30107, Spain

来源：

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS | 2021年 / 36卷 / 01期

关键词：

land labelling; online social networks; supervised learning; urban computing; urban mobility;

D O I：

10.1002/int.22307

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Nowadays, cities are the most relevant type of human settlement and their population has been endlessly growing for decades. At the same time, we are witnessing an explosion of digital data that capture many different aspects and details of city life. This allows detecting human mobility patterns in urban areas with more detail than ever before. In this context, based on the fusion of mobility data from different and heterogeneous sources, such as public transport, transport-network connectivity and Online Social Networks, this study puts forward a novel approach to uncover the actual land use of a city. Unlike previous solutions, our work avoids atime-invariantapproach and it considers the temporal factor based on the assumption that urban areas are not used by citizens all the time in the same manner. We have tested our solution in two different cities showing high accuracy rates.

引用

页码：478 / 525

页数：48

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