I-index for quantifying an urban location's irreplaceability

Wang, Xi; Chen, Jie; Pei, Tao; Ci, Song; Liu, Yaxi; Shu, Huan; Guo, Sihui; Xiao, Chen

doi:10.1016/j.compenvurbsys.2021.101711

Cited by 16 publications

(14 citation statements)

References 50 publications

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“…The area shape with the most significant promoting effect was similar with the Greek alphabet gamma. We speculated that since high-grade hospitals in Chengdu were mainly located in these areas, they were more irreplaceable in the candidates of residents’ travel destinations [ 58 ], thus bringing higher taxi pollutant emissions. Our findings confirmed again that the spatial variations in the effect of the urban built environment on taxis’ pollutant emissions should not be ignored.…”

Section: Resultsmentioning

confidence: 99%

Marginal Effects and Spatial Variations of the Impact of the Built Environment on Taxis’ Pollutant Emissions in Chengdu, China

Zhao

Pan

Yang

2022

IJERPH

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Understanding the impact of the urban built environment on taxis’ emissions is crucial for sustainable transportation. However, the marginal effects and spatial heterogeneity of this impact is worth noting. To this end, we calculated the taxis’ emissions on weekdays and weekends in Chengdu, China, and investigated the impact of the built environment on taxis’ emissions by applying multi-source data and several spatial regression models. The results showed that the taxis’ daily emissions on weekdays were higher than the emissions on weekends. The time heterogeneity of hourly taxis’ emissions was not significant, while the spatial heterogeneity of taxis’ emissions was significant. Except the HHI, the selected built environment variables both had a significant positive effect on taxis’ emissions on the global scale. There was a marginal effect of some built environment variables on taxis’ emissions, such as the density of bus stops and population density. The former exhibited an inhibitory effect on taxis’ emissions only when it was greater than 9 stops/km2, while the latter showed an inhibitory effect only in the range between 16,000 people/km2 and 22,000 people/km2. There were some spatial variations in the effects of built environment factors on taxis’ emissions, with HHI, road density, and accommodation service facilities density showing the most significant variation. The marginal effect and spatial variation of the impact needs to be considered when developing strategies to reduce taxis’ pollutant emissions.

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Section: Resultsmentioning

confidence: 99%

Marginal Effects and Spatial Variations of the Impact of the Built Environment on Taxis’ Pollutant Emissions in Chengdu, China

Zhao

Pan

Yang

2022

IJERPH

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“…From the perspective of people-place interaction, a highly attractive location tends to attract a large number of residents, and residents tend to travel long distances to visit the location (Wang et al, 2021; Arbués et al, 2016). This means that we need to consider both the flow volume and the flow length to define the edge weights (Wang et al, 2021), as shown belowwhere volij and lenij denote the volume and length of the flow between the commercial agglomeration i and source grid j, respectively. To avoid the effect of excessive outliers, the weights are normalized by the total volume and length of all the flows that reach the commercial agglomeration.…”

Section: Methodsmentioning

confidence: 99%

“…The most intuitive way to measure the attractiveness of a thing is to see how people react to it. For example, the number of people who buy can represent the attractiveness of goods, because people do “vote with their feet.” Big geo-data can objectively and quantitatively reflect human demand (Giglio et al, 2019; Liu et al, 2015; Wang et al, 2021). Compared with the above environmental supply, it is a more reasonable way to characterize attractiveness, and studying this supply–demand relationship is also helpful in understanding the human–environment relationship.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data

Huang

Yin

Xia

et al. 2023

Environment and Planning B: Urban Analytics and City Science

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Understanding the attractiveness of commercial agglomerations contributes to urban planning. Existing studies focus less on commercial agglomerations, and most directly use environmental supply factors to characterize attractiveness. This study measures attractiveness from the perspective of human demand. Specifically, we build a novel bipartite graph based on big geo-data of human mobility, using node centralities (degree, betweenness, and pagerank) to measure attractiveness. Next, we summarize multisource environmental features such as Point-of-Interests (POIs), land cover, transportation, and population, and use them as inputs to accurately predict attractiveness based on random forest. Finally, the spatial heterogeneity of the effects of these environmental variables on attractiveness is analyzed by multiscale geographically weighted regression. The results of the Beijing case show that: (1) All three centralities show a trend that the urban center is higher than the surrounding area, and betweenness is more reasonable. (2) Random forest can accurately predict attractiveness, with R2 for degree, betweenness, and pagerank at 0.903, 0.846, and 0.760, respectively. (3) The number of shopping POIs, the length of main roads, and the number of bus stops positively affect attractiveness, while the effects of greening ratio and population density are bidirectional. As for the service scope, about 70% of commercial agglomerations have an average service radius of less than 15 km, which is significantly correlated with the Voronoi diagram. Our results can inspire understanding the human–environment relationship and guide urban policymakers in business planning.

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“…It can accurately reflect the spatiotemporal distribution and changes in people's locations, obtain real-time flow information of individuals, and further explore urban residents' travel patterns and commuting modes (Liu et al, 2020(Liu et al, , 2022. Notably, Wang et al (2021) proposed the I-index to measure location's irreplaceability based on novel geospatial big data. This index considers both the flow of people and the distance using origin-destination (OD) flow data, quantifying the location's irreplaceability from a human-centric perspective (Wang et al, 2021).…”

Section: The Measurement Of Urban Location's Irreplaceabilitymentioning

confidence: 99%

Unraveling the impact of urban form on location's irreplaceability: An analysis from the perspective of spatiotemporal heterogeneity

Wu,

Gao,

et al. 2024

Transactions in GIS

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The concept of a location's irreplaceability pertains to its unique characteristics that make it challenging for other locations to replace in terms of providing services and meeting needs. While scholars have shown great enthusiasm for studying location irreplaceability, accurately measuring it and understanding the effects of urban form have not been thoroughly explored through empirical research. This study selects Shenzhen, China, as the study area. First, the I‐index, which focuses on geographic flow, is utilized to measure the location's irreplaceability using mobile phone data. Subsequently, the factors related to urban form are analyzed from the perspectives of demography, building capacity, public services, and the location's conditions. Finally, a geographically and temporally weighted regression model (GTWR) is constructed to explore the spatiotemporal relationships between irreplaceability and urban form. The study demonstrates that the I‐index, considering flow volume and distance, can effectively measure the location's irreplaceability. The results of GTWR indicate that the location's irreplaceability and its influencing factors exhibit spatiotemporal heterogeneity. This study advances our understanding of the relationships between location irreplaceability and urban form both conceptually and methodologically. The findings have practical implications for urban design and planning, including urban space optimization, facility equity, and the alleviation of traffic congestion.

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I-index for quantifying an urban location's irreplaceability

Cited by 16 publications

References 50 publications

Marginal Effects and Spatial Variations of the Impact of the Built Environment on Taxis’ Pollutant Emissions in Chengdu, China

Marginal Effects and Spatial Variations of the Impact of the Built Environment on Taxis’ Pollutant Emissions in Chengdu, China

Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data

Unraveling the impact of urban form on location's irreplaceability: An analysis from the perspective of spatiotemporal heterogeneity

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