Land-Use Degree and Spatial Autocorrelation Analysis in Kunming City Based on Big Data

Niu, Lede; Luo, Wang; Meiying, Jiang; Lü, Na

doi:10.1109/icitbs.2018.00033

Cited by 6 publications

(5 citation statements)

References 1 publication

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“…Local spatial autocorrelation is to analyze whether the local area specified attribute has spatial autocorrelation. The methods for describing local spatial autocorrelation are Local Moran's I (LISA) and Moran scatter plots [4] . (Fig.2 and Table 2), the maximum value is 4.53 in YunYan area, and the minimum value is 2.23 in XiuWen County; the range is 6.76, and the standard deviation is 2.09.…”

Section: Methodsmentioning

confidence: 99%

Spatial-temporal Pattern of Regional Economic Disparities in GuiYang

Yin

Shui

2018

E3S Web Conf.

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Based on the construction of indicator system for evaluating Comprehensive index of economic development, the synthetic economic development levels are evaluated from 2000 to 2015 in GuiYang. GIS Spatial analysis has studied the disparities in economic development in GuiYang. By calculating the spatial autocorrelation Moran's I index of GuiYang Comprehensive Development Index, it reveals that it's economic development has significant spatial autocorrelation, and the regional economic development has obvious dual structure; finally, through analyzing the local indicator of spatial association (LASA) each year. this paper reveals the temporal and spatial variation characteristics of economic development in GuiYang.

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Section: Methodsmentioning

confidence: 99%

Spatial-temporal Pattern of Regional Economic Disparities in GuiYang

Yin

Shui

2018

E3S Web Conf.

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“…First, application-driven approaches require strong reliance on raw data and analytics platforms. Representative methods include spatial auto-correlation analysis [16], nuclear density estimation (KDE) [17,18], cluster analysis [19], and social network analysis (SNA) [20]. In practice, there are many kinds of urban fragmentation data, and it is difficult to analyze the network structure and practical significance from multiple angles.…”

Section: Related Workmentioning

confidence: 99%

Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding

et al. 2020

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Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical significance of results. To explore the network structure and semantic relationships, we propose a general framework for multi-source spatio-temporal data analysis via knowledge graph embedding. The framework extracts low-dimensional feature representation from multi-source spatio-temporal data in a high-dimensional space, and recognizes the network structure and semantic relationships about multi-source spatio-temporal data. Experiment results show that the framework can not only effectively utilize multi-source spatio-temporal data, but also explore the network structure and semantic relationship. Taking real Shanghai datasets as an example, we confirm the validity of the multi-source spatio-temporal data analytical framework based on knowledge graph embedding.

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“…Generally, grassland degradation refers to the struggle to grow grassland structure, grassland species, and grassland products that are caused by overgrazing, cultivated land reclamation, climate change, policy change, rapid conversion of land-use types and fuel and herbs for medicine, and destruction by rodents, etc. [15,[74][75][76]. Grassland restoration refers to grassland renewal or regrowth, mainly including vegetation coverage change, soil conservation and remediation, and carbon sequestration [4,77].…”

Section: Evaluation Of Grassland Degradation and Restorationmentioning

confidence: 99%

An Analysis of Land-Use Change and Grassland Degradation from a Policy Perspective in Inner Mongolia, China, 1990–2015

Nacun

2018

Sustainability

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Land-use and land-cover changes have important effects on ecology, human systems, the environment, and policy at both global and regional scales. Thus, they are closely related to human activities. The extraction of more details about land-use change and grassland degradation is necessary to achieve future sustainable development in Inner Mongolia. The current study presents the patterns and processes of land-use changes over space and time, while also analyzing grassland degradation that is based on an analysis of land-use changes using a transition matrix, the Markov chain model and Moran’s I index, and a combination of long-time-scale remote sensing data as the data source. The major results indicate the following. (1) In 1990–2015, 13% (123,445 km2) of the total study area, including eight land-use types, changed. Woodland increased the most and moderate grassland decreased the most. (2) Grassland degradation, which occupied 2.8% of the total area of Inner Mongolia, was the major land-use conversion process before 2000, while, after 2000, 8.7% of the total area was restored; however, grassland degradation may still be the major ecological issue in Inner Mongolia. (3) Environmental protection policies show a close relationship with land-use conversion.

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Land-Use Degree and Spatial Autocorrelation Analysis in Kunming City Based on Big Data

Cited by 6 publications

References 1 publication

Spatial-temporal Pattern of Regional Economic Disparities in GuiYang

Spatial-temporal Pattern of Regional Economic Disparities in GuiYang

Urban Multi-Source Spatio-Temporal Data Analysis Aware Knowledge Graph Embedding

An Analysis of Land-Use Change and Grassland Degradation from a Policy Perspective in Inner Mongolia, China, 1990–2015

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