An interactive detector for spatial associations

Song, Yongze; Wu, Peng

doi:10.1080/13658816.2021.1882680

Cited by 42 publications

(12 citation statements)

References 52 publications

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“…Earth observation for more efficient and accurate modeling have been applied in improving the provision of urban green space for residents [192], minimizing urban-rural inequality in accessing public facilities [53,71], engaging rural and remote residents to work to decrease poverty incidences and achieve gender equality [1,193,194], etc. In addition, land, climate and environmental factors of infrastructure performance have been derived from Earth observation to estimate risks, disasters and future scenarios of performance [195,196].…”

Section: Typical Cases Of Eosimentioning

confidence: 99%

Earth Observation for Sustainable Infrastructure: A Review

Song

2021

Remote Sensing

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Infrastructure is a fundamental sector for sustainable development and Earth observation has great potentials for sustainable infrastructure development (SID). However, implementations of the timely, large–scale and multi–source Earth observation are still limited in satisfying the huge global requirements of SID. This study presents a systematical literature review to identify trends of Earth observation for sustainable infrastructure (EOSI), investigate the relationship between EOSI and Sustainable Development Goals (SDGs), and explore challenges and future directions of EOSI. Results reveal the close associations of infrastructure, urban development, ecosystems, climate, Earth observation and GIS in EOSI, and indicate their relationships. In addition, from the perspective of EOSI–SDGs relationship, the huge potentials of EOSI are demonstrated from the 70% of the infrastructure influenced targets that can be directly or indirectly derived from Earth observation data, but have not been included in current SDG indicators. Finally, typical EOSI cases are presented to indicate challenges and future research directions. This review emphasizes the contributions and potentials of Earth observation to SID and EOSI is a powerful pathway to deliver on SDGs.

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Section: Typical Cases Of Eosimentioning

confidence: 99%

Earth Observation for Sustainable Infrastructure: A Review

Song

2021

Remote Sensing

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“…Unlike the disadvantage of the global perception that considers the relationships between observation variables across geospatial units in a constant way, spatial heterogeneity investigating and portraying the associations between crime patterns and risk factors can help to unveil the complex crime generation mechanism ( Chen et al 2020 ; Boivin 2018 ). Correspondingly, leveraging the concept of strata, spatial stratified heterogeneity (SSH) as a type of quantifying the spatial heterogeneity, measures the association between dependent and explanatory variables by the variances in the stratified observations across geospatial strata ( Wang et al 2010 ; Wang et al 2016 ; Song and Wu 2021 ). The use of strata is ubiquitous in geospatial data types, such as urban function zones, climate zones, geographic divisions (Wang et al 2016 , 2010 , Wang and Hu 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal stratified associations between urban human activities and crime patterns: a case study in San Francisco around the COVID-19 stay-at-home mandate

et al. 2022

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Crime changes have been reported as a result of human routine activity shifting due to containment policies, such as stay-at-home (SAH) mandates during the COVID-19 pandemic. However, the way in which the manifestation of crime in both space and time is affected by dynamic human activities has not been explored in depth in empirical studies. Here, we aim to quantitatively measure the spatio-temporal stratified associations between crime patterns and human activities in the context of an unstable period of the ever-changing socio-demographic backcloth. We propose an analytical framework to detect the stratified associations between dynamic human activities and crimes in urban areas. In a case study of San Francisco, United States, we first identify human activity zones (HAZs) based on the similarity of daily footfall signatures on census block groups (CBGs). Then, we examine the spatial associations between crime spatial distributions at the CBG-level and the HAZs using spatial stratified heterogeneity statistical measurements. Thirdly, we use different temporal observation scales around the effective date of the SAH mandate during the COVID-19 pandemic to investigate the dynamic nature of the associations. The results reveal that the spatial patterns of most crime types are statistically significantly associated with that of human activities zones. Property crime exhibits a higher stratified association than violent crime across all temporal scales. Further, the strongest association is obtained with the eight-week time span centred around the SAH order. These findings not only enhance our understanding of the relationships between urban crime and human activities, but also offer insights into that tailored crime intervention strategies need to consider human activity variables.

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“…Spatial statistical inference is the basis of spatial analysis such as factor exploration and spatial prediction (Jacquez 1999;Møller 2013;Song and Wu 2021). Understanding distribution patterns and spatial association of geographical attributes is an essential approach for spatial statistical inference in geographical information science and mathematical geosciences (Hackeloeer et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…The spatial dependence principle assumes that values of attributes at near locations are more closely related than those at distant locations (Tobler 1970). The spatial dependence is usually quantified as spatial neighbor relations, lagged effects, or space-weighted matrix (Song and Wu 2021). The commonly used measures include spatial autocorrelation models (Moran 1950;Anselin 1995), spatial Bayesian hierarchical models (Haining and Haining 2003), geographically weighted regression and improved models (Brunsdon et al 1996;Fotheringham et al 2003), and singularity and anomaly models (Cheng 2007;Zuo et al 2009;Chen and Cheng 2016).…”

Section: Introductionmentioning

confidence: 99%

Geographically Optimal Similarity

Song

2022

Math Geosci

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Understanding geographical characteristics of distribution patterns and spatial association is essential for spatial statistical inference such as factor exploration and spatial prediction. The geographical similarity principle was recently developed to explain the association between geographical variables. It describes the comprehensive degree of approximation of a geographical structure instead of explicit relationships between variables. However, there are still challenges for geographical similarity-based methods. For instance, all samples are used for prediction, leading to increased calculation burden and reduced prediction accuracy due to the noise and unrelated data in large spatial data sets. This study develops a geographically optimal similarity (GOS) model for accurate and reliable spatial prediction based on the geographical similarity principle. In GOS, the geographical configurations are first characterized, and similarities between unknown and known observation locations are assessed. Next, an optimal threshold is determined to select a small number of observations with optimal similarities for the prediction at each unknown location. Finally, a reliable uncertainty assessment approach is developed to assess and map uncertainties of GOS predictions. A new R package “geosimilarity” is developed to conduct GOS models. In this study, GOS is implemented in predicting spatial distributions of trace elements in a mining region in Australia. Results show that GOS can use a small number of observations to derive more accurate and reliable spatial predictions than linear regression and basic configuration similarity models. In addition, pattern characteristics of predictions can be improved by GOS by eliminating the phenomenon wherein predictions are clustered near mean values and contain striped textures. Therefore, GOS demonstrates greater potential for implementing the geographical similarity principle in spatial predictions by bringing information from samples with relatively high similarities at any location across space for more accurate and effective predictions in broader fields and practice.

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An interactive detector for spatial associations

Cited by 42 publications

References 52 publications

Earth Observation for Sustainable Infrastructure: A Review

Earth Observation for Sustainable Infrastructure: A Review

Spatio-temporal stratified associations between urban human activities and crime patterns: a case study in San Francisco around the COVID-19 stay-at-home mandate

Geographically Optimal Similarity

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