Spatio-temporal autocorrelation measures for nonstationary series: A new temporally detrended spatio-temporal Moran's index

Shen, Chenhua; Li, Chaoling; Si, Ya-li

doi:10.1016/j.physleta.2015.09.039

Cited by 36 publications

(11 citation statements)

References 27 publications

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“…Spatial heterogeneity refers to the acquisition of different data caused by different spatial positions. Moran's I [33,34] is usually used to test the spatial heterogeneity of variables, and it is expressed as…”

Section: Analysis Of Spatial Heterogeneitymentioning

confidence: 99%

Spatiotemporal Characteristics of Bike-Sharing Usage around Rail Transit Stations: Evidence from Beijing, China

Wang

Cheng

et al. 2020

Sustainability

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As an emerging mode of transport, bike-sharing is being quickly accepted by Chinese residents due to its convenience and environmental friendliness. As hotspots for bike-sharing, railway-station service areas attract thousands of bikes during peak hours, which can block roads and pedestrian walkways. Of the many works devoted to the connection between bikes and rail, few have addressed the spatial‒temporal pattern of bike-sharing accumulating around station service areas. In this work, we investigate the distribution patterns of bike-sharing in station service areas, which are influenced not only by railway-station ridership but also by the built environment around the station, illustrating obvious spatial heterogeneity. To this end, we established a geographic weighted regression (GWR) model to capture this feature considering the variables of passenger flow and the built environment. Using the data from bike-sharing in Beijing, China, we applied the GWR model to carry out a spatiotemporal characteristic analysis of the relationship between bike-sharing usage in railway-station service areas and its determinants, including the passenger flow in stations, land use, bus lines, and road-network characteristics. The influence of these factors on bike-sharing usage is quite different in time and space. For instance, bus lines are a competing mode of transport with bike-sharing in suburban areas but not in city centers, whereas industrial and residential areas could also heavily affect the bike-sharing demand as well as railway-station ridership. The results of this work can help facilitate the dynamic allocation of bike-sharing and increase the efficiency of this emerging mode of transport.

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Section: Analysis Of Spatial Heterogeneitymentioning

confidence: 99%

Spatiotemporal Characteristics of Bike-Sharing Usage around Rail Transit Stations: Evidence from Beijing, China

Wang

Cheng

et al. 2020

Sustainability

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show abstract

“…Methodologies for using a spatiotemporal Moran's I index have been developed, and examine how spatial dependence patterns change over time or at different time scales. For example, Shen et al (2016) demonstrate how a temporally detrended global spatio-temporal Moran's I index, which accounts for temporal data that is not stationary, may be used to examine changes in the spatial and temporal dependence of daily precipitation data sets in China.…”

Section: Case Study 2: Incremental Spatial Autocorrelation Analysis Wmentioning

confidence: 99%

Applications of Spatial Autocorrelation Analyses for Marine Aquaculture Siting

et al. 2020

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Interest and growth in marine aquaculture are increasing around the world, and with it, advanced spatial planning approaches are needed to find suitable locations in an increasingly crowded ocean. Standard spatial planning approaches, such as a Multi-Criteria Decision Analysis (MCDA), may be challenging and time consuming to interpret in heavily utilized ocean spaces. Spatial autocorrelation, a statistical measure of spatial dependence, may be incorporated into the planning framework, which provides objectivity and assistance with the interpretation of spatial analysis results. Here, two case studies highlighting applications of spatial autocorrelation analyses in the northeast region of the United States of America are presented. The first case study demonstrates the use of a local indicator of spatial association analysis within a relative site suitability analysis-a variant of a MCDA-for siting a mussel longline farm. This case study statistically identified 17% of the area as highly suitable for a mussel longline farm, relative to other locations in the area of interest. The use of a clear, objective, and efficient analysis provides improved confidence for industry, coastal managers, and stakeholders planning marine aquaculture. The second case study presents an incremental spatial autocorrelation analysis with Moran's I that is performed on modeled and remotely sensed oceanographic data sets (e.g., chlorophyll a, sea surface temperature, and current speed). The results are used to establish a maximum area threshold for each oceanographic variable within the online decision support tool, OceanReports, which performs an automated spatial analysis for a user-selected area (i.e., drawn polygon) of ocean space. These thresholds provide users guidance and summary statistics of relevant oceanographic information for aquaculture planning. These two case studies highlight practical uses and the value of spatial autocorrelation analyses to improve the siting process for marine aquaculture.

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“…Spatial autocorrelation analysis is mainly applied to detect whether there is an aggregation trend between a spatial unit and its adjacent spatial units [29]. The global Moran's index (GMI) is a commonly used indicator for evaluating spatial autocorrelation statistics.…”

Section: Spatial Autocorrelation Analysismentioning

confidence: 99%

Spatiotemporal Variation and Pollution Assessment of Pb/Zn from Smelting Activities in China

Zang

et al. 2020

IJERPH

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Nonferrous smelting is an important source of heavy metals in soil, which causes different degrees of soil pollution, especially in lead–zinc (Pb/Zn) smelting areas. Based on the Pb/Zn environmental release during the Pb/Zn mineral smelting processes in 31 Chinese provinces from the period 2000 to 2015, the study analyzed the spatiotemporal variations in Pb/Zn environmental release at the national level and then evaluated the degree of soil pollution and potential ecological risk based on the heavy metal content in soil from Pb/Zn smelting areas. The results showed that from the period 2000 to 2015, the Pb release had a discrete trend, transferring from the middle east to the west, and the provinces with higher Pb releases were Henan Province, Yunnan Province, and Hunan Province. However, the Zn release showed a tendency towards spatial aggregation, and the release center of this heavy metal presented a shifting trend from south to north; additionally, the release of Zn was highest in Hunan Province. The pollution index analyses indicated that Cd, Pb, Cu, and Zn in Zhuzhou and Huludao all reached extremely polluted levels, while Tongguan was at a safe level. In Gejiu, Mianxian, Fengxian, Zhuzhou, Huludao, and Shaoguan, there were very high potential ecological risks, with Cd having the highest potential ecological risk in most smelting areas, followed by Pb.

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Spatio-temporal autocorrelation measures for nonstationary series: A new temporally detrended spatio-temporal Moran's index

Cited by 36 publications

References 27 publications

Spatiotemporal Characteristics of Bike-Sharing Usage around Rail Transit Stations: Evidence from Beijing, China

Spatiotemporal Characteristics of Bike-Sharing Usage around Rail Transit Stations: Evidence from Beijing, China

Applications of Spatial Autocorrelation Analyses for Marine Aquaculture Siting

Spatiotemporal Variation and Pollution Assessment of Pb/Zn from Smelting Activities in China

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