A functional seasonal thermal hot-spot classification: Focus on industrial sites

Guerri, Giulia; Crisci, Alfonso; Congedo, Luca; Munafò, Michele; Morabito, Marco

doi:10.1016/j.scitotenv.2021.151383

Cited by 23 publications

(9 citation statements)

References 131 publications

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“…Further analysis of the spatio-temporal separate pattern was based on Anselin Local Moran's I model [32].…”

Section: Data Sources and Preprocessingmentioning

confidence: 99%

Coupling of Population and Settlement Change Trends in Traditional Rural Areas and Urban–rural Development Spatial Patterns: A Case Study of Heilongjiang Province, China

Lin,

Du,

Zhang

et al. 2024

Preprint

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Achieving the dual development of rural revitalization and urbanization is an important strategic goal of China’s current modernization. Rural population and settlements are the main components of the countryside in traditional agricultural areas, and play an important role in the development of the countryside itself in terms of urban and rural land use and in the formation of urban and rural development patterns. This study analyzes the spatial and temporal changes in rural populations and settlements at the township level, as well as the degree and type of coupled development. A framework for urban–rural development relationships based on different types of coupled development is proposed, and a model of urban–rural forces is constructed to derive the spatial pattern of urban–rural development at the township level that may be formed in the future. Our study shows that the rural population and settlements in the study area are characterized by significant spatial and temporal dynamics, indicating that traditional rural areas are in a process of rapid development and change. The results of the township-level coupling measurements present four types of development in terms of the spatial pattern of urban–rural development: A-type is most likely to produce new cities or satellite towns in the Jixi–Harbin–Qiqihar area in the future; B-type is the fastest dying area in the future, located in the Harbin and Qitaihe clusters, with a tendency to merge into Harbin; C-type is the area to be focused on in the future to attract population and strictly control the growth of the rural settlement area to avoid it turning into B-type; D-type may develop regional township centers in the future in the combined area of Jiamusi City, Suihua City and Mudanjiang City. The results of the study provide experience in realizing rural revitalization according to local conditions. The new spatial pattern of urban–rural development provides an important reference for formulating land use policies, optimizing urban–rural land allocation and realizing integrated urban–rural development strategies.

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“…Further analysis of the spatio-temporal separate pattern was based on Anselin Local Moran's I model [32].…”

Section: Data Sources and Preprocessingmentioning

confidence: 99%

Coupling of Population and Settlement Change Trends in Traditional Rural Areas and Urban–rural Development Spatial Patterns: A Case Study of Heilongjiang Province, China

Lin,

Du,

Zhang

et al. 2024

Preprint

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“…Further details on LST, albedo, and SVF retrieval methods were described in previous studies [20,21].…”

Section: Characterization Of Residential Buildings Located In Differe...mentioning

confidence: 99%

“…We are aware of the limitation of our study due to the buffer area size which restricts the analysis within 50 m of distance and introduces an arbitrary element as a function of the building size. However, previous studies applied similar buffer areas to evaluate the influence of the daytime summer LST on urban feature concentrations surrounding residential and industrial buildings [21,42]. As reported by recent studies [21,37,40,68,69], favorable buffers ranging from about 50 m and 300 m were used to investigate the potential LST-related thermal drivers on local urban scales.…”

Section: Study Limitationmentioning

confidence: 99%

“…However, previous studies applied similar buffer areas to evaluate the influence of the daytime summer LST on urban feature concentrations surrounding residential and industrial buildings [21,42]. As reported by recent studies [21,37,40,68,69], favorable buffers ranging from about 50 m and 300 m were used to investigate the potential LST-related thermal drivers on local urban scales.…”

Section: Study Limitationmentioning

confidence: 99%

“…This study aims to investigate the variation of residential buildings' real estate value in the municipal area of Florence (Tuscany, Italy) in relation to the summer intra-urban surface thermal pattern [20,21], also accounting for the contribution of urban infrastructures (such as the presence of grassy and arboreal surfaces, land consumption, and water bodies) surrounding (considering different buffer areas) residential buildings. The surface thermal pattern layer is represented by clusters of hot-and cool-spot zones obtained by applying a robust statistical spatial method based on the summer land surface temperature layer (Landsat-8 remote sensing data).…”

Section: Introductionmentioning

confidence: 99%

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Residential Buildings’ Real Estate Values Linked to Summer Surface Thermal Anomaly Patterns and Urban Features: A Florence (Italy) Case Study

et al. 2022

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Climate-change-related extreme events impact ecosystems, people, economy, and infrastructures, with important consequences on the real estate market as well. This study aims to investigate the variation of residential buildings’ real estate values in a historic Italian city in relation to the summer surface thermal anomaly pattern and urban features surrounding buildings. Open data from remote sensing products and the national database of the Revenue Agency of Italy were used. Real estate values of residential buildings were spatially analyzed in four urban belts, and the association with daytime summer surface hot- and cool-spot zones was studied through odds ratio (OR) statistic. Urban features (impervious area, tree cover, grassland area, and water body) surrounding residential buildings with different real estate values were also analyzed. Considering the whole Florentine municipality, 13.0% of residential buildings fell into hot-spot zones (only 0.6% into cool-spot ones), characterized by very low tree cover surfaces (generally <1%), most of which were in the central belt (37% of all buildings in central belt). Almost 10% of these buildings belonged to the highest market value class revealing a positive association (OR = 1.53) with hot-spot zones. This study provides useful information to plan targeted building interventions to avoid a probable decrease of the value of residential properties in high heat-related risk areas.

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Spatial Distribution Pattern of Aromia bungii Within China and Its Potential Distribution Under Climate Change and Human Activity

Zhang,

Wang,

Xie

et al. 2024

Ecology and Evolution

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Aromia bungii is a pest that interferes with the health of forests and hinders the development of the fruit tree industry, and its spread is influenced by changes in abiotic factors and human activities. Therefore, exploring their spatial distribution patterns and potential distribution areas under such conditions is crucial for maintaining forest ecosystem security. This study analyzed the spatial differentiation characteristics of the geographic distribution pattern of A. bungii in China using Moran's I and the Getis‐Ord General G index. Hot spot distribution areas were identified using Getis‐Ord Gi*. An optimized MaxEnt model was used to predict the potential distribution areas of A. bungii within China under four shared economic pathways by combining multivariate environmental data: (1) prediction of natural environmental variables predicted under current climate models; (2) prediction of natural environmental variables + human activities under current climate models; and (3) prediction of natural environmental variables under the future climate models (2050s and 2070s). Meanwhile, MigClim was used to simulate the unoccupied suitable area in the presence of obstacles under future climate change. The results showed that human activities, minimum temperature of the coldest month, and precipitation of the wettest month had positive effects on the distribution of A. bungii. However, in the current period, human activities drastically reduced the survival area of A. bungii, and its suitable distribution area was mainly concentrated in the eastern and central regions of China. Under the influence of climate change in the future, the habitat of A. bungii will gradually increase. Additionally, the MigClim model indicates that the area unoccupied by A. bungii has been on a continuous increasing trend. This study provides a positive reference for the prevention and control of A. bungii and the maintenance of forest health and ecosystem security, and provides important theoretical guidance for researchers, policymakers, and governments.

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A functional seasonal thermal hot-spot classification: Focus on industrial sites

Cited by 23 publications

References 131 publications

Coupling of Population and Settlement Change Trends in Traditional Rural Areas and Urban–rural Development Spatial Patterns: A Case Study of Heilongjiang Province, China

Coupling of Population and Settlement Change Trends in Traditional Rural Areas and Urban–rural Development Spatial Patterns: A Case Study of Heilongjiang Province, China

Residential Buildings’ Real Estate Values Linked to Summer Surface Thermal Anomaly Patterns and Urban Features: A Florence (Italy) Case Study

Spatial Distribution Pattern of Aromia bungii Within China and Its Potential Distribution Under Climate Change and Human Activity

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