Exploiting mobile phone data for multi-category land use classification in Africa

Mao, Huina; Thakur, Gautam; Bhaduri, Budhendra

doi:10.1145/3007540.3007549

Cited by 10 publications

(20 citation statements)

References 11 publications

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“…With an increasing usage of mobile phone in modern cities, mobile big data has been widely utilized for urban analysis, such as estimation of travel demand (Toole et al, 2015), analysis of the distribution of economy activities (Chang et al, 2014), home detection (Vanhoof et al, 2018), classification of land use and land cover types (Mao et al, 2016) and understanding of individual mobility patterns. Apart from mobile phone data, night-time light (NTL) remote sensing which reflects human activities is important for studying social issues such as poverty, environment, and ecology (Hu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Recognition of Urban Functional Regions at Street Scale Based on Lj1-01 Night-Time Light Remote Sensing and Mobile Big Data

Zhou

Zhang

Sun

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Night-time light (NTL) remote sensing data has been widely used in the analysis of human activities at night for a large extent, such as light pollution monitoring and recognition of urban functional regions. In most previous studies, the spatial resolutions of NTL remote sensing data are rather low (e.g., 500 m or coarser). Besides, remote sensing classification of land use rather than land cover types is often a hard task. Due to the reasons, it is difficult to meet the demand of urban refined management. In this study, mobile big data and Luojia1-01 (LJ1-01) NTL remote sensing satellite data with higher spatial resolution are adopted for extracting urban functional regions at the street-level scale. Taking Shenzhen city as a case, mobile big data (i.e., the volume of mobile devices) with the accuracy of approximate 150 m (i.e., 7-bit GeoHash encoding format) is integrated with NTL remote sensing data. After a hot spot analysis, the correlation between the two types of data are analysed. The typical supervised classification algorithms including KNN, SVM and random forest are used to distinguish urban functional regions, which are represented by five types, namely residential, business and commercial, industrial, transportation and other areas. The results show the effectiveness in extracting land use types in cities by combination of Luojia1-01 night-time light remote sensing and mobile big data. This study provides a basis for monitoring night light pollution of residential area, urban planning and the construction of smart cities.

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Section: Introductionmentioning

confidence: 99%

Recognition of Urban Functional Regions at Street Scale Based on Lj1-01 Night-Time Light Remote Sensing and Mobile Big Data

Zhou

Zhang

Sun

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…These data sets provide detailed and accurate results of population maps of dynamic population flow [ 42 , 43 , 44 ], age structure change [ 45 , 46 , 47 ], urbanization development [ 48 , 49 , 50 ], building or settlement characteristic information [ 51 , 52 , 53 ], and greatly promote the cross-study of population spatialization. By combining with other related fields, important data and method support are provided to guide the urban planning [ 42 , 54 ], to assess the risk of demographic risk [ 55 , 56 ], and to improve the population quality of life [ 57 , 58 ].…”

Section: Introductionmentioning

confidence: 99%

“…Facing thefact that the fusion of more and more data sources, the variety method of population spatialization and the difference perspective of population research, it is a very important direction of the future research to use suitable data and establish the population spatialization method to meet the needs of different administrative units. At present, many scholars have carried out a series of researches on data process and methods for the improvement of the data source precision [ 54 ], the cross validation of population spatialization method [ 45 , 47 ], and the evaluation of the experimental results [ 31 ]. Few people pay attention to the demand and difference of the population spatialization method under different administrative units.…”

Section: Introductionmentioning

confidence: 99%

Methods of Population Spatialization Based on the Classification Information of Buildings from China’s First National Geoinformation Survey in Urban Area: A Case Study of Wuchang District, Wuhan City, China

Jiang

et al. 2018

Sensors

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Most of the currently mature methods that are used globally for population spatialization are researched on a single level, and are dependent on the spatial relationship between population and land covers (city, road, water area, etc.), resulting in difficulties in data acquisition and an inability to identify precise features on the different levels. This paper proposes a multi-level population spatialization method on the different administrative levels with the support of China’s first national geoinformation survey, and then considers several approaches to verify the results of the multi-level method. This paper aims to establish a multi-level population spatialization method that is suitable for the administrative division of districts and streets. It is assumed that the same residential house has the same population density on the district level. Based on this assumption, the least squares regression model is used to obtain the optimized prediction model and accurate population space prediction results by dynamically segmenting and aggregating house categories.In addition, it is assumed that the distribution of the population is relatively regular in communities that are spatially close to each other, and that the population densities on the street level are similar, so the average population density is assessed by optimizing the community and surrounding residential houses on the street level. Finally, the scientificalness and rationality of the proposed method is proved by spatial autocorrelation analysis, overlay analysis, cross-validation analysis and accuracy assessment methods.

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“…Land use information plays an important role in urban planning and can inform city design and utility distribution [2]. Land use refers to the function of the land, which is shaped by human activities [14], such as education, retail, etc. It is different from land cover, such as vegetation, built-up areas, etc., which is determined by the land's physical attributes.…”

Section: Introductionmentioning

confidence: 99%

“…A key challenge in evaluating LU classification is the lack of ground truth. POI data has therefore also been used as reference set [14] although its validity as ground truth is not clear.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use

Deng

Newsam

2017

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics

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This paper performs a quantitative comparison of open-source data available on the Internet for the fine-grain mapping of land use. Three points of interest (POI) data sources-Google Places, Bing Maps, and the Yellow Pages-and one volunteered geographic information data source-Open Street Map (OSM)-are compared with each other at the parcel level for San Francisco with respect to a proposed fine-grain land-use taxonomy. The sources are also compared to coarse-grain authoritative data which we consider to be the ground truth. Results show limited agreement among the data sources as well as limited accuracy with respect to the authoritative data even at coarse class granularity. We conclude that POI and OSM data do not appear to be sufficient alone for fine-grain land-use mapping. CCS CONCEPTS• Information systems → Geographic information systems;• Networks → Location based services; KEYWORDSLand use, points of interest, volunteered geographic information ACM Reference Format:

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Exploiting mobile phone data for multi-category land use classification in Africa

Cited by 10 publications

References 11 publications

Recognition of Urban Functional Regions at Street Scale Based on Lj1-01 Night-Time Light Remote Sensing and Mobile Big Data

Recognition of Urban Functional Regions at Street Scale Based on Lj1-01 Night-Time Light Remote Sensing and Mobile Big Data

Methods of Population Spatialization Based on the Classification Information of Buildings from China’s First National Geoinformation Survey in Urban Area: A Case Study of Wuchang District, Wuhan City, China

Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use

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