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Wang F.,Guangzhou University | Wang F.,Guangdong Key Laboratory for Urbanization and Geo Simulation | Zhuo L.,Guangdong Key Laboratory for Urbanization and Geo Simulation | Zhuo L.,Sun Yat Sen University | And 5 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2015

Liquid bioenergy from energy plants is a promising sustainable energy source in China. But, due to a huge population, it is impossible to use many cultivated lands to expand the planting area of liquid bioenergy to safeguard food security and meet rapidly increasing energy demand. Planting the energy plants on marginal lands in China is an important approach to develop liquid bioenergy industry and solve the supply problem of raw materials. It can be also helpful to relieve the pressure of carbon emission reduction and protect the environment. However, the availability and the multi-suitability of marginal lands, especially at a regional scale, remain uncertain. Accurate evaluation of marginal land potential for energy plants growing is a serious part for rational planning and utilization of marginal lands. Existing land multi-suitability evaluation models are mostly linear models, which often cause overestimation or underestimation of single ecological factor. A nonlinear ecological multi-suitability evaluation model was developed in this study, which introduces expert knowledge and uses Gauss curve to match the optimum ecological niche of the marginal land based on the demand of ecological niche of the energy plant. Matching degree is used as fitness value to measure the multi-suitability and potential of the marginal land for planting energy plants. Taking Guangdong Province as an example, 4 species of liquid energy plants, namely Cassava, Vernicia fordii, Jatropha curcas L. and Pistacia chinensis, were chosen to evaluate the potential of liquid bioenergy. Using a supervised classification process, we identified the marginal lands from the Landsat TM/ETM satellite images with a 30 m resolution that cover Guangdong. The multi-suitability of the marginal lands for planting the 4 species was evaluated using the ecological niche models (ENMs), which incorporate local temperature, precipitation, soil, terrain and traffic. Our modeling consisted of 2 steps, one at the individual ecological factor level, and the other at the habitat level that integrated the outputs from the individual factors. Three kinds of situations were considered, which were represented by 3 types of function curves, namely the S-shaped, Bell-shaped, and Z-shaped curve. The S-shaped curve characterizes the situation that large ecological factor values favor the crop. The Z-shaped curve indicates that small factor values favor the crop. The Bell-shaped curve defines a most optimal range in the factor value, and the optimality drops as the factor value deviates from this range. The results support the following conclusions: the total area of the marginal land in Guangdong is 106.35×104 hm2, and the shrub land, open forest land, and grassland account for 31.36%, 43.86% and 22.43%, respectively. The shoal/bottomland and unused land only account for very small proportion. With abundant heat and water resources, good soil condition and rich marginal land resource, Guangdong Province has high multi-suitability of planting energy plants and great potential of developing liquid bioenergy industry. The area of marginal land suitable for planting the 4 species is 62.15×104 hm2, which accounts for 58.44% of the total marginal land. Ecological niche fitness value for Jatropha curcas L. and Pistacia chinensis is the highest, and for these 2 species have similar ecological niche, there is a competition between them for development space; the second is Vernicia fordii; Cassava has the lowest niche fitness value, which is suitable for growing on the west terrace plain and east coastal hilly tableland in Guangdong Province. ©, 2015, Chinese Society of Agricultural Engineering. All right reserved.


Zhou S.,Sun Yat Sen University | Zhou S.,Guangdong Key Laboratory for Urbanization and Geo Simulation | Deng L.,Guangzhou Panyu Urban Planning and Design Institute | Huang M.,University College London
Chinese Geographical Science | Year: 2013

Metropolitan cities in China are commonly confronted with unresolved traffic congestion issues, primarily due to rapidly increasing traffic demand. Group disparity between commuting mode choice and its spatial distribution on road networks has enabled us to examine the factors that give rise to the discrepancies and the fundamental spatial causes of traffic congestion. In recent years, micro-perspective, individual, and behavior-based spatial analysis have mushroomed and been facilitated with effective tools such as temporal geographic information systems (T-GIS). It is difficult to study the interrelations between transport and space on the basis of commuting mode choice since the mode choice data are invisible in a specific space such as a particular road network. Therefore, in the field of transport, the classical origin destination (OD) four-stage model (FSM) is usually employed to calculate data when studying commuting mode choice. Based on the relative principles of T-GIS and the platform of ArcGIS, this paper considers Guangzhou as a case study and develops a spatio-temporal tool to examine the daily activities of residents. Meanwhile, the traffic volume distribution in rush hours, which was analyzed according to commuting modes and how they were reflected in the road network, was scrutinized with data extracted from travel diaries. Moreover, efforts were made to explain the relationship between traffic demand and urban spatial structure. Based on the investigation, this research indicates that traffic volumes in divergent groups and on the road networks is driven by: 1) the socio-economic characteristics of travelers; 2) a jobs-housing imbalance under suburbanization; 3) differences in the spatial supply of transport modes; 4) the remains of the Danwei (work unit) system and market development in China; and 5) the transition of urban spatial structure and other factors. © 2013 Science Press, Northeast Institute of Geography and Agricultural Ecology, CAS and Springer-Verlag Berlin Heidelberg.


Zhou S.,Sun Yat Sen University | Zhou S.,Guangdong Key Laboratory for Urbanization and Geo Simulation | Xie M.,United Investment Group Co. | Kwan M.-P.,University of Illinois at Urbana - Champaign | Kwan M.-P.,University Utrecht
Habitat International | Year: 2015

The social and spatial patterns of ageing population in urban China illuminate the influence of the socioeconomic transformation associated with the dual economic system. Guangzhou, one of China's megacities, is used as a case study in this paper. Data from the 2010 National Census were used to examine the spatial differentiation and the related factors of ageing communities in Guangzhou. Using social area analysis, the study identified six categories of ageing communities: traditional inner-city communities, traditional danwei residential communities, commercial housing ageing communities, danwei compound ageing communities, immigrant ageing communities, and rural ageing communities. The paper analyses the reasons and processes leading to the clustering of the elderly population in each category of areas associated with distinctive spatial patterns of ageing differentiation. These patterns suggest that the socio-spatial differentiation of ageing communities is a joint outcome of urban development, housing policies, personal status and family relationships. The dynamics of both ageing in place and ageing with migration had a dual impact on the spatial, social and ecological patterns of ageing communities. The spatial differentiation of ageing communities in the study area should be taken into account when formulating urban planning and public policies. In addition, a dynamic public facility and service allocation system is also necessary to meet the needs associated with evolving socio-spatial urban restructuring. © 2015 Elsevier Ltd.


Guo T.,Sun Yat Sen University | Zhang X.,Sun Yat Sen University | Zhang X.,Guangdong Key Laboratory for Urbanization and Geo Simulation
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | Year: 2013

To realize automatic extraction of change information in GIS vector data updating, a change information recognition method based on neural network decision tree is proposed. A change information hierarchical detection method based on quad-tree was designed and realized in this paper. With the comparison of vertex-edge characteristics, this method could rapidly locate change areas. Based on the matching of correspondent objects, a neural network decision tree method was applied to recognize the change information. This method combined the effective logical judgment of decision tree and adaptive processing of neural network. 1:2000 Topographical data were used to verify the effectiveness of the method. Experimental result shows that this method can achieve high computing speed and effectively detect the change pattern of vector data, which can improve the automation and intelligence level of dynamic updating in GIS database.


Luo G.,Sun Yat Sen University | Luo G.,Guangxi Teachers Education University | Zhang X.,Sun Yat Sen University | Zhang X.,Guangdong Key Laboratory for Urbanization and Geo Simulation | Guo T.,Sun Yat Sen University
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | Year: 2014

The change-only information is important to the recording of object life cycle, the establishment of spatial-temporal database and the updating of GIS database. To solve the problem of low efficiency of traditional method in change detection when the data volume is large, we proposed a highly efficient method of change detection based on the grid-partitioning of data and the comparison of synthesis of spatial and attribute information. This method only detects the changed grid to reduce the detection region. In order to solve the matching problem of old features and new features, we propose a method named optimal combination-matching method. The method selects the optimally matched features through the comparison of the characteristic of spatial information and semantic information. The method's high efficiency and accuracy in change detection of large volume of spatial data and matching of changed features is validated by experiment. ©, 2014, SinoMaps Press. All right reserved.

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