Chinese progress in geomorphometry

Lv, Guonian; Xiong, Liyang; Chen, Min; Tang, Guoan; Sheng, Yehua; Liu, Xuejun; Song, Zhiyao; Yu-qi, LU; Yu, Zhaoyuan; Ka, Zhang; Wang, Meizhen

doi:10.1007/s11442-017-1442-0

Cited by 48 publications

(27 citation statements)

References 64 publications

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“…The additional information on these factors and their calculations are presented in Table 2 [3,4,39,40]. Among these factors, terrain relief can demonstrate the topographic environment of a county [39], cultivated land quality and water resource abundance can express its natural resource endowments [4], and road network density and location index can be used to represent its accessibility and economic location [33,40], respectively. These five spatial influential factors are expected to highlight the spatial disparities of poverty-stricken counties in China.…”

Section: Calculation Of Spatial Influential Factorsmentioning

confidence: 99%

“…This system employs the factors of terrain relief (X 1 ), cultivated land quality (X 2 ), and water resource abundance (X 3 ) as natural influential constraints and the factors of road network density (X 4 ) and location index (X 5 ) as social influential constraints [4]. The additional information on these factors and their calculations are presented in Table 2 [3,4,39,40]. Among these factors, terrain relief can demonstrate the topographic environment of a county [39], cultivated land quality and water resource abundance can express its natural resource endowments [4], and road network density and location index can be used to represent its accessibility and economic location [33,40], respectively.…”

Section: Calculation Of Spatial Influential Factorsmentioning

confidence: 99%

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Modeling the Spatial Formation Mechanism of Poverty-Stricken Counties in China by Using Geographical Detector

et al. 2019

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The poverty-stricken counties in China follow a spatial pattern of regional poverty. Examining the influential factors of this spatial pattern can provide an important reference that can guide China in its implementation of a poverty alleviation policy. By applying a geographical detector and using a sample of poverty-stricken counties in China, this study explores the spatial relationship of county distribution with spatial influential factors, including terrain relief, cultivated land quality, water resource abundance, road network density, and the locational index. These poverty-stricken counties are then classified, and the main factors that restrict their economic development are determined. The results highlight that the selected poverty-stricken counties suffer a severe condition in each of the spatial factors mentioned above. Most of these counties are classified under the location index, terrain relief, and road network density constraint types. Each of the aforementioned spatial influential factors has unique controlling mechanisms on the distribution of these poverty-stricken counties. Most of these counties are constrained by two or multiple spatial influential factors, except for some counties located in South and Central China, which are mainly constrained by a single spatial influential factor. Therefore, these single factor-constrained poverty-stricken counties warrant more attention when a developmental policy for poverty alleviation is to be implemented. The various aspects of poverty-stricken counties constrained by multiple factors must be comprehensively considered with a special focus on their development. The differentiated policies must be designed for these poverty-stricken counties on the basis of their spatial influential factors.

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Section: Calculation Of Spatial Influential Factorsmentioning

confidence: 99%

Section: Calculation Of Spatial Influential Factorsmentioning

confidence: 99%

Modeling the Spatial Formation Mechanism of Poverty-Stricken Counties in China by Using Geographical Detector

et al. 2019

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“…The decentralization of urban factors provides a new dimension slope [54], and curvature [55] are common indicators used to analyze and depict topographic characteristics. Over the past years, the DEM has also been successfully applied in social science areas, including the spatial distribution of urban factors and urban planning [20,42,52]. However, compared with the application of DEM in natural science, some application fields in the social sciences still need to be further explored, such as the characterization of complex spatial morphology, three-dimensional analysis, visualization, and so on.…”

Section: Introductionmentioning

confidence: 99%

“…However, these studies do not focus on urban housing prices and are limited to geo-visual analytics.Supported by GIS, the spatial interpolation methods have been widely used to produce the digital elevation model (DEM) and the digital terrain model (DTM). Most research uses limited topographic elevation data to estimate the digital expression of the topographic change, elevation change, and surface feature change [52]. The traditional digital terrain model has been widely used in natural science research fields such as land, hydrology, and meteorology, and surface roughness [53],…”

mentioning

confidence: 99%

Complex Spatial Morphology of Urban Housing Price Based on Digital Elevation Model: A Case Study of Wuhan City, China

Zuo

Zhou

et al. 2019

Sustainability

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In a city, housing price varies with location. Thus, housing price plays an important role in detecting the spatial pattern of the city. Spatial interpolation methods have been widely used for simulating and predicting urban housing prices. In this paper, the Ordinary Kriging interpolation method is used for producing the digital elevation model (DEM) of urban housing prices. Based on the three-dimensional DEM of urban housing price, this paper develops a novel approach for geo–visual analytics of urban housing prices. To investigate and visualize the spatial morphology of housing price, we design the Water-flooding, Section-cutting and Belt-floating methods, and implement these methods with the 3D-analyst module in GIS environment. Then, we take Wuhan City as a case, apply this approach to analyze the complex spatial morphologic characteristics of the DEM for housing price and visualize the results from the multidimensional perspectives. The results show that the Water-flooding method effectively supports the investigation of the top areas of surface changes; Section-cutting method performs well in examining the profile or cross-section of the urban housing surface; and Belt-floating method is helpful for detecting the spatial variance of the urban housing surface through the routes of specific lines. The results demonstrate that the proposed approach works better than traditional methods in describing the complex spatial morphology of urban housing prices, and has an advantage in visualizing the analysis results.

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“…The depressions can seriously influence the accuracy of stream extraction if the depression‐filling process is not performed (Tarboton, Bras, & Rodriguez‐Iturbe, ). Depression filling ensures that no local minima exist in a DEM, implying that every cell has a monotonically descending flow path to the edge of the DEM; thus, an accurate and continuous drainage network could be extracted during the hydrological analysis (Lv et al, ). Currently, a common step toward achieving a reasonable drainage network is to remove the depressions in DEMs at the beginning of a hydrological analysis project.…”

Section: Introductionmentioning

confidence: 99%

Improved Priority‐Flood method for depression filling by redundant calculation optimization in local micro‐relief areas

Xiong

Jiang

et al. 2019

Transactions in GIS

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Depression filling is a critical step in distributed hydrological modeling using digital elevation models (DEMs). The traditional Priority‐Flood (PF) approach is widely used due to its relatively high efficiency when dealing with a small‐sized DEM. However, it seems inadequate and inefficient when dealing with large high‐resolution DEMs. In this work, we examined the relationship between the PF algorithm calculation process and the topographical characteristics of depressions, and found significant redundant calculations in the local micro‐relief areas in the conventional PF algorithm. As such calculations require more time when dealing with large DEMs, we thus propose a new variant of the PF algorithm, wherein redundant points and calculations are recognized and eliminated based on the local micro‐relief water‐flow characteristics of the depression‐filling process. In addition, depressions and flatlands were optimally processed by a quick queue to improve the efficiency of the process. The proposed method was applied and validated in eight case areas using the Shuttle Radar Topography Mission digital elevation model (SRTM‐DEM) with 1 arc‐second resolution. These selected areas have different data sizes. A comparative analysis among the proposed method, the Wang and Liu‐based PF, the improved Barnes‐based PF, the improved Zhou‐based PF, and the Planchon and Darboux (P&D) algorithms was conducted to evaluate the accuracy and efficiency of the proposed algorithm. The results showed that the proposed algorithm is 43.2% (maximum) faster than Wang and Liu's variant of the PF method, with an average of 31.8%. In addition, the proposed algorithm achieved similar performance to the improved Zhou‐based PF algorithm, though our algorithm has the advantage of being simpler. The optimal strategies using the proposed algorithm can be employed in various landforms with high efficiency. The proposed method can also achieve good depression filling, even with large amounts of DEM data.

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Chinese progress in geomorphometry

Cited by 48 publications

References 64 publications

Modeling the Spatial Formation Mechanism of Poverty-Stricken Counties in China by Using Geographical Detector

Modeling the Spatial Formation Mechanism of Poverty-Stricken Counties in China by Using Geographical Detector

Complex Spatial Morphology of Urban Housing Price Based on Digital Elevation Model: A Case Study of Wuhan City, China

Improved Priority‐Flood method for depression filling by redundant calculation optimization in local micro‐relief areas

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