Towards a new urban geography of expenditure: Using bank card transactions data to analyze multi-sector spatiotemporal distributions

Carpio-Pinedo, José; Romanillos, Gustavo; Aparicio, Daniel; Martín-Caro, María Soledad Hernández; Palomares, Juan Carlos García; Puebla, Javier Gutiérrez

doi:10.1016/j.cities.2022.103894

Cited by 11 publications

(7 citation statements)

References 47 publications

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“…This study chose a 1500 m border in Sari's outer environment (buffer) as a rural area. Based on this, the following equation [41] was also extracted to produce the UHII:…”

Section: Methodsmentioning

confidence: 99%

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Spatiotemporal Analysis of Urban Heat Islands and Vegetation Cover Using Emerging Hotspot Analysis in a Humid Subtropical Climate

Ghanghermeh,

Roshan,

Asadi

et al. 2024

Atmosphere

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Research on the temporal and spatial changes of the urban heat island effect can help us better understand how urbanization, climate change, and the environment are interconnected. This study uses a spatiotemporal analysis method that couples the Emerging Hot Spot Analysis (EHSA) technique with the Mann–Kendall technique. The method is applied to determine the intensity of the heat island effect in humid subtropical climates over time and space. The data used in this research include thermal bands, red band (RED) and near-infrared band (NIR), and Landsat 7 and 8 satellites, which were selected from 2000 to 2022 for the city of Sari, an Iranian city on the Caspian Sea. Pre-processed spectral bands from the ‘Google Earth Engine’ database were used to estimate the land surface temperature. The land surface temperature difference between the urban environment and the outer buffer (1500 m) was modeled and simulated. The results of this paper show the accuracy and novelty of using Emerging Hotspot Analysis to evaluate the effect of vegetation cover on the urban heat island intensity. Based on the Normalized Difference Vegetation Index (NDVI), the city’s land surface temperature increased by approximately 0.30 °C between 2011 and 2022 compared to 2001 to 2010. However, the intensity of the urban heat island decreased during the study period, with r = −0.42, so an average −0.031 °C/decade decrease has been experienced. The methodology can be transferred to other cities to evaluate the role of urban green spaces in reducing heat stress and to estimate the heat budget based on historical observations.

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“…This study chose a 1500 m border in Sari's outer environment (buffer) as a rural area. Based on this, the following equation [41] was also extracted to produce the UHII:…”

Section: Methodsmentioning

confidence: 99%

“…The UHII anomaly was then estimated using the Getis-Ord Gi statistic and is determined as follows [41]:…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Analysis of Urban Heat Islands and Vegetation Cover Using Emerging Hotspot Analysis in a Humid Subtropical Climate

Ghanghermeh,

Roshan,

Asadi

et al. 2024

Atmosphere

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“…This classification resulted in five distinct groups. To note is that Madrid lends itself for our case study because of its high socio-economic segregation (Leal and Sorando 2017;Musterd et al 2017) and the diversity of commercial environments (Carpio-Pinedo 2020;Carpio-Pinedo et al 2022;Carpio-Pinedo and Gutiérrez 2020).…”

Section: Methodsmentioning

confidence: 99%

Activity Reduction as Resilience Indicator: Evidence with Filomena Data

Santiago-Iglesias,

Schmöcker,

Carpio-Pinedo

et al. 2023

Findings

Self Cite

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We explore how different socioeconomic groups adapt to the snowstorm Filomena that occurred in Madrid in 2021. A reverse interpretation of the resilience triangle is proposed, where smaller triangle areas indicate less resilient populations continuing to travel despite disruptions, while larger demand losses indicate greater adaptability. Using Google Popular Times data, the study measures activity levels in neighborhoods, focusing on essential and non-essential trips. Vulnerable groups with lower incomes show limited adaptability, resulting in a smaller reduction in commercial activities compared to affluent neighborhoods. The study emphasizes the significance of considering demand adaptability and socioeconomic factors in assessing resilience.

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“…Carpio-Pinedo et al analyzed the relationship between commercial spatial distribution and social space by using open cadastral, business registration, and social network data [30]. Romanillos et al analyzed inter-sectoral business activities based on transaction data sourced from bank cards [31]. Chen et al used POI data to study the correlation between the entity retail industry and commercial economic distribution [32].…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial Distribution Characteristics and Influencing Factors on the Retail Industry in the Central Urban Area of Lanzhou City at the Scale of Daily Living Circles

Lu,

Yu,

Xin

et al. 2023

IJGI

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Using a people-centered approach to new urbanization, China has committed to building high-quality living environments through improving urban livability and promoting a stronger sense of belonging among residents. Retail stores serve as one of the most immediate and accessible destinations for residents’ consumption, and their spatial configuration has a direct impact on residents’ satisfaction and happiness in their daily lives. In this context, for the present study we selected the central urban area of Lanzhou City as the case study area. Based on POI data and using the daily life circle as the basic unit, we applied methods such as kernel density analysis, hotspot analysis, and the Shannon–Weaver index to analyze spatial distribution patterns of the retail industry. Furthermore, we applied Geodetector to analyze the impacts of four factors that are closely related to the retail industry: economic level, convenience level, market demand, and location. The conclusions are as follows: In the central urban area of Lanzhou, the retail industry exhibits a belt distribution pattern along the Yellow River. The density of distribution gradually decreases from the city center toward the outskirts, forming four prominent agglomeration centers. Overall, within the central urban area of Lanzhou, the spatial distribution of the retail industry at the scale of daily living circles shows that only a small proportion of the industry demonstrates noticeable clustering effects. In terms of spatial patterns, the retail industry at the scale of the daily living circles demonstrates similar characteristics in terms of diversity and agglomeration distribution. It exhibits a decreasing trend from the urban core toward the peripheral areas. The agglomeration distribution pattern of the retail industry in the central urban area of Lanzhou is considerably influenced by market demand, economic level, convenience, and location. The spatial distribution of the retail industry in the central urban area is primarily influenced by economic factors and convenience, while market demand plays a major role and location has a relatively minimal impact.

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Towards a new urban geography of expenditure: Using bank card transactions data to analyze multi-sector spatiotemporal distributions

Cited by 11 publications

References 47 publications

Spatiotemporal Analysis of Urban Heat Islands and Vegetation Cover Using Emerging Hotspot Analysis in a Humid Subtropical Climate

Spatiotemporal Analysis of Urban Heat Islands and Vegetation Cover Using Emerging Hotspot Analysis in a Humid Subtropical Climate

Activity Reduction as Resilience Indicator: Evidence with Filomena Data

Spatial Distribution Characteristics and Influencing Factors on the Retail Industry in the Central Urban Area of Lanzhou City at the Scale of Daily Living Circles

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