Identification of low-carbon travel block based on GIS hotspot analysis using spatial distribution learning algorithm

Hou, Quanhua; Zhang, Xuan; Li, Bo; Zhang, Xiaoqing; Wang, Wenhui

doi:10.1007/s00521-018-3447-8

Cited by 37 publications

(12 citation statements)

References 14 publications

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“…First, the authors established a database of geospatial attributes, based on the latitudes and longitudes obtained from Google Earth. Then, the KDE algorithm was selected from the Spatial Analyst Toolbox of ArcGIS 10.2, and used to measure the spatial distribution density of UGGp in China [25]. On this basis, the kernel density map of the seven geographical regions was generated (Figure 4), in which the number (density) of UGGp in an area is positively correlated with the darkness of the local color.…”

Section: Hotspots Of Spatial Distributionmentioning

confidence: 99%

Spatial Distribution of UNESCO Global Geoparks in China and Its Influencing Factors

Ding¹,

Li²,

Dai³

et al. 2020

IJDNE

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China is an active responder to the UNESCO Geopark Program, and owns a total of 39 UNESCO Global Geoparks (UGGp). This paper firstly reviews the history of UGGp in China, and systematically analyzes their spatial distribution with Gini coefficient, Lorentz curve and kernel density estimation (KDE). Next, the influencing factors of the spatial distribution were examined through spatial regression. The main results are as follows: The density of UGGp in China decreases from the east to the west and from the south to the north. The spatial distribution of UGGp in China is extremely non-uniform: most are concentrated in East, Central and Southwest China, while a few are located in Northeast and Northwest China. Spatial regression results indicate that the regional/provincial distribution of UGGp is significantly affected by government support, tourism and infrastructure, slightly affected by landform, and weakly affected by the distribution of geosites. In future, China should prioritize the construction and application of UGGp in geosites with international significance in Northwest and Northeast China, and the government at all levels must provide more supports to developing geosites with regional features.

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Section: Hotspots Of Spatial Distributionmentioning

confidence: 99%

Spatial Distribution of UNESCO Global Geoparks in China and Its Influencing Factors

Ding¹,

Li²,

Dai³

et al. 2020

IJDNE

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“…We can use the Z-score and p-value to obtain the location of the high-value (hotspot) or low-value (cold-spot) clustering to generate the Hotspot Analysis Image Data. The Z-score and p-value are statistical measures of hotspots and cold-spots used to judge whether to reject the null hypothesis on a factor-by-element basis [41][42][43][44][45].…”

Section: Extract the Mountain Vertices Region Of Dem Based On The Hotmentioning

confidence: 99%

A Rapid and High-Precision Mountain Vertex Extraction Method Based on Hotspot Analysis Clustering and Improved Eight-Connected Extraction Algorithms for Digital Elevation Models

et al. 2020

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Digital Elevation Model (DEM)-based mountain vertex extraction is one of the most useful DEM applications, providing important information to properly characterize topographic features. Current vertex-extraction techniques have considerable limitations, such as yielding low-accuracy results and generating false mountain vertices. To overcome these limitations, a new approach is proposed that combines Hotspot Analysis Clustering and the Improved Eight-Connected Extraction algorithms that would quickly and accurately provide the location and elevation of mountain vertices. The use of the elevation-based Hotspot Analysis Clustering Algorithm allows the fast partitioning of the mountain vertex area, which significantly reduces data and considerably improves the efficiency of mountain vertex extraction. The algorithm also minimizes false mountain vertices, which can be problematic in valleys, ridges, and other rugged terrains. The Eight-Connected Extraction Algorithm also hastens the precise determination of vertex location and elevation, providing a better balance between accuracy and efficiency in vertex extraction. The proposed approach was used and tested on seven different datasets and was compared against traditional vertex extraction methods. The results of the quantitative evaluation show that the proposed approach yielded higher efficiency, considerably minimized the occurrence of invalid points, and generated higher vertex extraction accuracy compared to other traditional methods.

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“…However, in order to guide the sustainable development of urban transport, some researchers have proposed to use evolutionary game theory as a framework to study the travel mode choices and the impacts of transportation policy changes on the equilibrium of traffic systems [ 26 ]. Some advanced research methods, such as geographic information system (GIS), are also used to improve the accuracy of choices prediction [ 27 ]. In comparison, most of these studies do not focus on low-carbon traffic and SEM is still the main method in the study of travel mode choices considering low-carbon factors.…”

Section: Introductionmentioning

confidence: 99%

Chaos Analysis of Urban Low-Carbon Traffic Based on Game Theory

Ren

2021

IJERPH

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Developing urban low-carbon traffic is an effective measure to reduce traffic carbon emissions, which are important parts of greenhouse gas. In order to understand the development characteristics and regular patterns of urban low-carbon traffic, we present a game model that enables us to predict the possible range of travel mode choice and the impact of low-carbon awareness. Through chaos analysis and simulation of the model, the authors come to realize that the proportions of travel mode choice can reach an equilibrium under a certain urban traffic system. This equilibrium is related to low-carbon awareness and the situation of the urban traffic system. The research we have done suggests that in small cities with undeveloped traffic systems, the most effective measure to achieve urban low-carbon traffic is to increase the comprehensive costs of high-carbon travel. However, in big cities with developed traffic systems, raising low-carbon awareness of residents can greatly increase the proportion of low-carbon travelers and improve the stability of travel mode choice. The results could provide development strategies and policy suggestions for urban low-carbon traffic and reduce the adverse impact of urban traffic emissions on public health.

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Identification of low-carbon travel block based on GIS hotspot analysis using spatial distribution learning algorithm

Cited by 37 publications

References 14 publications

Spatial Distribution of UNESCO Global Geoparks in China and Its Influencing Factors

Spatial Distribution of UNESCO Global Geoparks in China and Its Influencing Factors

A Rapid and High-Precision Mountain Vertex Extraction Method Based on Hotspot Analysis Clustering and Improved Eight-Connected Extraction Algorithms for Digital Elevation Models

Chaos Analysis of Urban Low-Carbon Traffic Based on Game Theory

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