An investigation on thermal patterns in Iran based on spatial autocorrelation

Ghalhari, Gholamabbas Fallah; Roudbari, AbbasAli Dadashi

doi:10.1007/s00704-016-2015-3

Cited by 20 publications

(4 citation statements)

References 36 publications

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“…Therefore, in the case of significant global autocorrelation results, a local spatial autocorrelation analysis method is introduced to characterize the spatial dependence and spatial heterogeneity of specific regions. It is defined as follows [50,51]:…”

Section: ) Local Spatial Autocorrelation (Local Moran's I)mentioning

confidence: 99%

Research on Temporal and Spatial Differentiation and Impact Paths of Agricultural Grey Water Footprints in the Yellow River Basin

Shi

Hao

et al. 2022

Water

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The scientific evaluation of water pollution in the Yellow River Basin was directly related to the sustainable utilization of water resources and the green development of the agricultural economy in this region. In this study, we focused on the planting industry, and measured the agricultural grey water footprint of 73 prefecture-level cities in the Yellow River Basin from 2000 to 2019. We used spatial autocorrelation analysis to reveal temporal and spatial differentiation characteristics, and we used the path analysis method to study the factors influencing the temporal evolution and spatial distribution. Taking 2015 as the study period, the agricultural grey water footprint showed a trend of first rising and then falling. The values and growth rates of the agricultural grey water footprint in different regions were quite different. According to the natural breakpoint method, the agricultural grey water footprints were divided into low, middle, high, and very high groups. There were obvious spatial differences in the agricultural grey water footprints, and these differences gradually decreased. Generally, the H–L and the L–L types were dominant. From 2000 to 2019, most prefecture-level cities maintained the same transition changes as those in the neighboring regions. Crop yield, economic scale, population scale, urban and rural structure, and technological innovation were found to be the key elements of spatiotemporal variation in the agricultural grey water footprint.

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Section: ) Local Spatial Autocorrelation (Local Moran's I)mentioning

confidence: 99%

Research on Temporal and Spatial Differentiation and Impact Paths of Agricultural Grey Water Footprints in the Yellow River Basin

Shi

Hao

et al. 2022

Water

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“…Clustering diversity of Iran in other studies has been confirmed, which is in line with findings of other studies. 48,60 By evaluating these climatic clusters, the largest study cluster of 32.4% of frequencies occurred at stations in the first cluster. The second and fourth cluster, had 2.7% frequencies, and were introduced as the smallest clusters.…”

Section: Summary Of Main Findingsmentioning

confidence: 99%

Climatic clustering analysis for novel atlas mapping and bioclimatic design recommendations

Roshan

Farrokhzad

Attia

2019

Indoor and Built Environment

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Defining and determining climatic zones accurately is crucial to inform the decision making of building designers and planners during early design phases of urban development. Characterizing the climatic zones allows estimation of energy requirements in buildings and develop climate adapted energy polices. Climatic zones can be defined by using the statistical cluster analysis. Data from weather stations can be used after standardization with zero mean and unit variance, to confirm that all variables are weighted equally in the cluster analysis. In this paper, a novel atlas for 19 climatic zones is presented that represent a variety of bioclimatic design strategies and recommendations, for passive design, based on a clustering analysis in Iran. The clustering analysis is based on the statistical analysis of daily temperature and relative humidity from 1995 to 2014. The results visualize 19 different climate zones for Iran and indicate the dominance of passive design strategies. As a result, Iran was divided into eight climatic clusters. The results showed that each of the studied clusters require specific strategies in providing indoor comfort. The outputs of this study shed the light on the importance of up-to-date climate characterization and the effectiveness of climate mapping and recommendations to inform decision makers.

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“…These methods are used to identify trends and patterns in voter behavior and to predict the outcome of an election based on voter preferences and voting patterns [22,23,24,25]. Spatial auto-correlation methods were used in numerous fields of research including geography, plant populations, traffic crashes, landscape patterns, drought propagation, satellite data, migration flows, tectonics, image segmentation, COVID-19 spread patterns, thermal patterns and in almost all fields of applied sciences [26,27,28,29,30,31,32,33,34,35]. Many models from statistical physics were used in the analysis of spatial relations between electoral turnouts and outcomes [36,37,38,39], however, there is a gap in the literature in utilizing spatial auto-correlation techniques in the study of entropy distribution in electoral outcomes.…”

Section: Introductionmentioning

confidence: 99%

Entropic Spatial Auto-correlation of Voter Uncertainty and Voter Transitions in Parliamentary Elections

Deeb¹

2023

Preprint

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This paper studies a novel spatial auto-correlation model of voter uncertainty across districts. We use the Moran I index to measure the auto-correlation of Shannon, relative Shannon, Tsallis and relative Tsallis entropies of regional electoral outcomes with respect to geographic adjacency, proximity and sectarian adjacency. Using data from the Lebanese parliamentary elections, we find strong geographic and gravitational adjacency correlations. More importantly, there is a notably strong correlation in sectarian adjacency in both 2018 and 2022 elections, with a very high level of confidence. This result asserts the dominance of the sectarian factor in Lebanese politics. We also introduce the method of maximized general entropy estimation that allows us to determine the Markov transition matrix of voters between consecutive elections.

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An investigation on thermal patterns in Iran based on spatial autocorrelation

Cited by 20 publications

References 36 publications

Research on Temporal and Spatial Differentiation and Impact Paths of Agricultural Grey Water Footprints in the Yellow River Basin

Research on Temporal and Spatial Differentiation and Impact Paths of Agricultural Grey Water Footprints in the Yellow River Basin

Climatic clustering analysis for novel atlas mapping and bioclimatic design recommendations

Entropic Spatial Auto-correlation of Voter Uncertainty and Voter Transitions in Parliamentary Elections

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