Spatial-Temporal Analysis of Point Distribution Pattern of Schools Using Spatial Autocorrelation Indices in Bojnourd City

Ghodousi, Mostafa; Sadeghi‐Niaraki, Abolghasem; Rabiee, Farzaneh; Choi, Soo-Mi

doi:10.3390/su12187755

Cited by 19 publications

(9 citation statements)

References 30 publications

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“…Average nearest neighbor analysis (ANN) was proposed by Philip Clark and Francis Evans [ 21 ] in 1954 to determine the clustering characteristics of points. This method can be used to verify the development of building clusters [ 23 , 24 ]. The average nearest-neighbor ratio (the ratio of the average observed distance to the average expected distance) of individuals is calculated in this study.…”

Section: Methodsmentioning

confidence: 99%

Morphological Pattern of Building Clusters in Cold Regions: Evidence from Harbin

Pan

Yang

et al. 2022

IJERPH

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The rapidly changing global conditions of the environment and climate have resulted in higher requirements for urban design. Significant annual temperature variations and large day/night temperature differences in cold-region cities leads to high energy consumption. Therefore, it is challenging to achieve low energy consumption in cold-region cities. Urban morphology focuses on the physical elements of urban areas, reflecting the relationship between the city and its environment and the city’s response to natural climatic conditions. Building clusters are common in cold regions due to the extreme climate. Thus, it is crucial to study the energy performance of cities by considering urban morphology. This study focuses on four morphological patterns of building clusters: point, linear, courtyard, and mixed patterns. A case study is conducted in Harbin, a cold-region city in China. Samples of the four morphological patterns are extracted, and GIS analysis and manual labeling are used to analyze the dominant morphological patterns of building clusters in cold regions. Average nearest-neighbor analysis is used to obtain quantitative results and determine the prevalence of different morphological patterns of building clusters in cold regions. This process can be used to determine the dominant patterns of urban building clusters and provide a scientific basis for selecting the morphological patterns of new building clusters in cold regions.

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Section: Methodsmentioning

confidence: 99%

Morphological Pattern of Building Clusters in Cold Regions: Evidence from Harbin

Pan

Yang

et al. 2022

IJERPH

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“…To explore the spatial dependence pattern (SDP) of PM 2.5 concentrations, the global autocorrelation Moran's I and hot/cold spots method were determined at the administrative unit level to characterize the global and local spatial agglomeration [40]. Global autocorrelation Moran's I [−1,1] was calculated as follows:…”

Section: Spatial Autocorrelation Methodsmentioning

confidence: 99%

“…Values above zero indicate hot spots while values below zero indicate cold spots. Given the empirical selection from previous studies and the suitability comparison of measurement results from different methods, the spatial relations and spatial weights were constructed using the Fixed Distance and the Euclidean distance methods [40].…”

Section: Spatial Autocorrelation Methodsmentioning

confidence: 99%

The Driving Influence of Multi-Dimensional Urbanization on PM2.5 Concentrations in Africa: New Evidence from Multi-Source Remote Sensing Data, 2000–2018

Wei

Sun

Jiang

et al. 2021

IJERPH

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Africa’s PM2.5 pollution has become a security hazard, but the understanding of the varying effects of urbanization on driven mechanisms of PM2.5 concentrations under the rapid urbanization remains largely insufficient. Compared with the direct impact, the spillover effect of urbanization on PM2.5 concentrations in adjacent regions was underestimated. Urbanization is highly multi-dimensional phenomenon and previous studies have rarely distinguished the different driving influence and interactions of multi-dimensional urbanization on PM2.5 concentrations in Africa. This study combined grid and administrative units to explore the spatio-temporal change, spatial dependence patterns, and evolution trend of PM2.5 concentrations and multi-dimensional urbanization in Africa. The differential influence and interaction effects of multi-dimensional urbanization on PM2.5 concentrations under Africa’s rapid urbanization was further analyzed. The results show that the positive spatial dependence of PM2.5 concentrations gradually increased over the study period 2000–2018. The areas with PM2.5 concentrations exceeding 35 μg/m3 increased by 2.2%, and 36.78% of the African continent had an increasing trend in Theil–Sen index. Urbanization was found to be the main driving factor causing PM2.5 concentrations changes, and economic urbanization had a stronger influence on air quality than land urbanization or population urbanization. Compared with the direct effect, the spillover effect of urbanization on PM2.5 concentrations in two adjacent regions was stronger, particularly in terms of economic urbanization. The spatial distribution of PM2.5 concentrations resulted from the interaction of multi-dimensional urbanization. The interaction of urbanization of any two different dimensions exhibited a nonlinear enhancement effect on PM2.5 concentrations. Given the differential impact of multi-dimensional urbanization on PM2.5 concentrations inside and outside the region, this research provides support for the cross-regional joint control strategies of air pollution in Africa. The findings also indicate that PM2.5 pollution control should not only focus on urban economic development strategies but should be an optimized integration of multiple mitigation strategies, such as improving residents’ lifestyles, optimizing land spatial structure, and upgrading the industrial structure.

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“…In Eq. ( 3), xi is a value for feature i, X is the mean of the descriptive values, wi,j is the spatial weight between two features i and j where the sum of weights is 1, and n is the total number of features (Ghodousi, et al 2020). This method is important for identifying spatial association patterns because it can identify smaller geographic areas where positive or negative clustering occurs.…”

Section: Local Moran's Imentioning

confidence: 99%

Obesity; Spatial Accessibility and SEIFA Case Study: Greater Melbourne Area (GMA)

Hatami

Qadikolaei

Namin

2023

Preprint

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Today, the inequality of distribution of built environments leads to the formation of advantaged and disadvantaged neighborhoods. Advantage neighborhoods have good accessibility (Distance to) and availability (Number of) service centers. If the neighborhoods have some service centers that don’t provide healthy lifestyles, especially in increasing obesity, they can decline community health in these areas. So, this research tries to have a spatial view of obesity as a dependent variable. Independent variables are the number of and distance to food, smoking, and physical activity centers that are based on theoretical concepts. We analyzed them with dependent variables on SEIFA Clusters at SA2 level. This research has used spatial analysis methods such as BILISA Cluster on Local Moran I for clustering, GWR for spatial correlation which is a base of the analysis method. The results in the Great Melbourne Area (GMA) show that the level of accessibility is more important than availability and some SA2s in the low levels of SEIFA haven’t good access to a healthy built environment and which makes them more obese.

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Spatial-Temporal Analysis of Point Distribution Pattern of Schools Using Spatial Autocorrelation Indices in Bojnourd City

Cited by 19 publications

References 30 publications

Morphological Pattern of Building Clusters in Cold Regions: Evidence from Harbin

Morphological Pattern of Building Clusters in Cold Regions: Evidence from Harbin

The Driving Influence of Multi-Dimensional Urbanization on PM2.5 Concentrations in Africa: New Evidence from Multi-Source Remote Sensing Data, 2000–2018

Obesity; Spatial Accessibility and SEIFA Case Study: Greater Melbourne Area (GMA)

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