Reducing spatial autocorrelation in the dynamic simulation of urban growth using eigenvector spatial filtering

Yan, Xinlei; Feng, Yongjiu; Tong, Xiaohua; Li, Pengshuo; Zhou, Yilun; Wu, Peiqi; Xie, Huan; Yan-ming, Jin; Chen, Peng; Liu, Shijie; Xv, Xiong; Wang, Chao

doi:10.1016/j.jag.2021.102434

Cited by 11 publications

(2 citation statements)

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“…The spatiotemporal cubic pattern analysis method and time series clustering model were used to investigate the distribution of cycling rhythm throughout the day within urban area limits [45][46][47]. First, the study area was divided into a grid of 500 m in size, in which each cube was constructed based on this grid with the same length and width at a time interval of 30 min.…”

Section: Spatiotemporal Rhythm Analysis Modelmentioning

confidence: 99%

Spatiotemporal Polyrhythm Characteristics of Public Bicycle Mobility in Urban Chronotopes Context

Chen

Jiang

2021

IJGI

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Cycling rhythm performance is the result of a complex interplay between active travel demand and cycling network supply. Most studies focused on bicycle flow, but little attention has been paid to cycling rhythm changes for public bicycles. Full sample data of origin–destination enables an efficient description of network-wide cycling mobility efficiency in urban public bicycle systems. In this paper, we show how the spatiotemporal characteristics of cycling speed reveal the performance of cycling rhythms. The inference method of riding speed estimation is proposed with an unknown cycling path. The significant inconsistency of docking stations in cycling rhythm was unraveled by the source–sink relationship comparison. The asymmetry of the cycling rhythm on the path is manifested as the rhythm difference among paths and bidirectional inconsistency. We found that cycling rhythm has a temporal multilayer and spatial mismatch, which shows the inflection points of the cycling rhythm where the travel behavioral preference changes and the exact road segments with different rhythms. This finding suggests that a well-designed cycling environment and occupation-residential function should be considered in active transport demand management and urban planning to help induce active travel behavior decisions.

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Section: Spatiotemporal Rhythm Analysis Modelmentioning

confidence: 99%

Spatiotemporal Polyrhythm Characteristics of Public Bicycle Mobility in Urban Chronotopes Context

Chen

Jiang

2021

IJGI

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“…Various types of thermal and optical bands from satellite imagery like MODIS, Landsat, Sentinel are available with different spatial and temporal resolutions to calculate LST and create LULC maps (Chaves et al, 2020). GIS software offers spatial statistics tools such as Spatial Autocorrelation that help analyse the distribution and relationship of geographic features (Bao et al, 2000;Yan et al, 2021). Therefore, remote sensing can be utilised to investigate temperature increases at the surface as well as their general trends of either their increase or decrease.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Characterization Of Land Surface Temperature In Relation Landuse/Cover: A Spatial Autocorrelation Approach

Diksha

Kumari

2023

Journal of Landscape Ecology

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The land use and land cover (LULC) characteristics of Ghaziabad have experienced dynamic changes because of the city's ongoing industrialization and urbanisation processes. These shifts can be directly attributed to human actions. These shifts can be directly attributed to human actions. Thermal variation in the study area necessitates LULC analysis. Landsat and Sentinel satellite data for 2011 and 2021 were used to map LULC, estimate land surface temperature (LST) and analysis spatial autocorrelation among the variables using ArcGIS software and the Google Earth Engine (GEE) cloud platform. A sharp descent is observed in the cropland while built-up area has increased during the study period. With the increase in the built-up surface in the area, the ambient temperatures have also increased from 18.70 °C in 2011 to 21.81 °C in 2021 leading to urban heat island effect. At all spatial scales, spatial autocorrelation is a characteristic property of most ecological parameters. The spatial clustering of LST in an ecosystem can play a crucial role in determining the dynamics of LULC.The Moran’s, I show that there is a considerable level of spatial autocorrelation in the values of LST and highly clustered pattern for both the years. Monitoring and understanding the surface thermal environment is crucial to discerning the causes of climate change.

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Novel spatial models for analysis the long-term impact of LULC changes on hydrological components at sub-basin level

Sakizadeh

Milewski

2023

Environ Monit Assess

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Reducing spatial autocorrelation in the dynamic simulation of urban growth using eigenvector spatial filtering

Cited by 11 publications

References 50 publications

Spatiotemporal Polyrhythm Characteristics of Public Bicycle Mobility in Urban Chronotopes Context

Spatiotemporal Polyrhythm Characteristics of Public Bicycle Mobility in Urban Chronotopes Context

Spatiotemporal Characterization Of Land Surface Temperature In Relation Landuse/Cover: A Spatial Autocorrelation Approach

Novel spatial models for analysis the long-term impact of LULC changes on hydrological components at sub-basin level

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