Roles of locational factors in the rise and fall of restaurants: A case study of Beijing with POI data

Wu, Mingbo; Pei, Tao; Wang, Wenlai; Guo, Sihui; Ci, Song; Chen, Jie; Zhou, Chenghu

doi:10.1016/j.cities.2021.103185

Cited by 47 publications

(38 citation statements)

References 42 publications

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“…In this study, the predictive results were accurate by using data generated from Google Places API to solve outdated data from the government sector that was prepared the following fiscal year. We used Google Places API to solve this problem, such as a study by Wu et al [42] that found that using POI data from the private sector produced data analysis results with more accuracy.…”

Section: Discussionmentioning

confidence: 99%

A Study on Estimating Land Value Distribution for the Talingchan District, Bangkok Using Points-of-Interest Data and Machine Learning Classification

2021

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Land is an essential factor in real estate developments, and each location has its unique characteristics. Land value is a vital cost of real estate developments. Higher land costs mean that project developers must create higher valued products to cover the higher land costs and to maintain a profit level from their developments. Land values vary according to surrounding factors, such as environment, social, and economic situations. Machine learning is a popular data estimation technique that enables a system to learn from sample data; however, there are few studies on its use for estimating land value distribution. Therefore, we aim to apply the technique of machine learning to estimate land value and to investigate the factors affecting the land value in the Talingchan district, Bangkok., we used land value level as the dependent variable, with other factors affecting land value levels as the independent variables. Ten points of interest were chosen from Google Places API. Then, three machine learning algorithms, namely CART, random forest, support vector machine, were applied. For this study, we selected 45,032 land parcels as the experimental data and randomly divided them into two groups. The first 70% of the land parcels was used to create the training area. The other 30% of the land parcels was used to create the testing area to verify the accuracy of the land value estimation from the applied machine learning techniques. The most accurate machine learning results were produced by random forest, which were then used to measure the factor importance. The academic group factor was school, and the commercial group factors were clothing store, pharmacy, convenience store, hawker stall, grocery store, automatic teller machine, supermarket, restaurant, and company.

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Section: Discussionmentioning

confidence: 99%

A Study on Estimating Land Value Distribution for the Talingchan District, Bangkok Using Points-of-Interest Data and Machine Learning Classification

2021

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“…The equations should be inserted in an editable format from the equation editor. The field intensity model is derived from a physical concept and is mostly used to determine the division of the radiation and development range of urban hinterlands (Wu et al, 2020;Wu et al, 2021). The field intensity model was selected to measure the service capacity of Japanese cuisine in different cities.…”

Section: Field Intensity Modelmentioning

confidence: 99%

Exotic Food, Food Environment, and Geographical Patterns: Big Data Analytics From Japanese Cuisine in China

Tian

Luan²,

Wang³

2022

Front. Earth Sci.

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As urban amenity welfare, exotic food is related to consumers’ equal access to diversified food and a healthy diet. This study uses big data provided by an online catering platform to explore Japanese cuisine in China. The field intensity model and spatial econometric model are used to analyze the place effect and its relationship with local socioeconomic factors. The results illustrate that 1) the spatial distribution of Japanese cuisine shows the characteristics of an unbalanced agglomeration distribution, with the coastal economically developed cities as the key layout area and gradually extending to inland cities. 2) Price characteristics indicate that the service target of Japanese cuisine is mainly the middle class. In addition, the spatial inequality of field intensity value indicates that wealthy Eastern cities have more opportunities to enjoy more kinds of and higher quality exotic food. 3) In the local socioeconomic environment, urbanization level, population size, and economic scale are significantly related to inequal access to Japanese cuisine. The essential mechanism of these circumstances is the internal needs of pricing characteristics and the negative externalities caused by unequal urban infrastructure.

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“…With the rapid development and wide application of Internet Big Data, data sources represented by POI provide new possibilities for evaluating facility service capabilities [14,[28][29][30]. These new data types have been widely used in measuring the spatial layout of M&H land/facilities [15], public toilets [31], restaurants [32], stadiums [8], green space [33], etc. AOI is very similar to POI, but the difference is that AOI is a polygon file, which can clearly represent the shape and boundary of the land.…”

Section: Introductionmentioning

confidence: 99%

The Spatial Distribution and Optimization of Medical and Health Land from the Perspective of Public Service Equalization: A Case Study of Urumqi City

Yan

et al. 2022

Sustainability

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From the two aspects of land quantity and spatial distribution, this article studies the existing problems and ideas for optimizing the supply of medical and health (M&H) land for municipal units to promote an equal supply of urban public services. Method: The existing problems were explored with the help of the kernel density, the spatial gravity model and the buffer zone analysis method, and the key optimization areas of M&H land under the trends of population flow were explored by constructing a suitability evaluation system for the natural, social and ecological elements. Results: The total amount of M&H land in the study area was lower than the standard. The characteristics of land supply that support hospitals and primary medical care are different, which makes it difficult for the population in different regions to obtain services from the two types of medical facilities. The supply of both types of land has room for improvement. Conclusion: The effect of public M&H supply is greatly affected by the factors of land supply, which directly causes the problem of uneven medical services in different regions. The land-use layout should be scientifically planned according to the characteristics of different regions.

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Roles of locational factors in the rise and fall of restaurants: A case study of Beijing with POI data

Cited by 47 publications

References 42 publications

A Study on Estimating Land Value Distribution for the Talingchan District, Bangkok Using Points-of-Interest Data and Machine Learning Classification

A Study on Estimating Land Value Distribution for the Talingchan District, Bangkok Using Points-of-Interest Data and Machine Learning Classification

Exotic Food, Food Environment, and Geographical Patterns: Big Data Analytics From Japanese Cuisine in China

The Spatial Distribution and Optimization of Medical and Health Land from the Perspective of Public Service Equalization: A Case Study of Urumqi City

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