2015 Vol. 25, No. 3
Display Method:
2015, 25(3): 263-273.
doi: 10.1007/s11769-015-0754-1
Abstract:
Using the low-resolution (T31, equivalent to 3.75°×3.75°) version of the Community Earth System Model (CESM) from the National Center for Atmospheric Research (NCAR), a global climate simulation was carried out with fixed external forcing factors (1850 Common Era. (C.E.) conditions) for the past 2000 years. Based on the simulated results, spatio-temporal structures of surface air temperature, precipitation and internal variability, such as the El Niño-Southern Oscillation (ENSO), the Atlantic Multi-decadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO), and the North Atlantic Oscillation (NAO), were compared with reanalysis datasets to evaluate the model performance. The results are as follows:1) CESM showed a good performance in the long-term simulation and no significant climate drift over the past 2000 years;2) climatological patterns of global and regional climate changes simulated by the CESM were reasonable compared with the reanalysis datasets;and 3) the CESM simulated internal natural variability of the climate system performs very well. The model not only reproduced the periodicity of ENSO, AMO and PDO events but also the 3-8 years variability of the ENSO. The spatial distribution of the CESM-simulated NAO was also similar to the observed. However, because of weaker total irradiation and greenhouse gas concentration forcing in the simulation than the present, the model performances had some differences from the observations. Generally, the CESM showed a good performance in simulating the global climate and internal natural variability of the climate system. This paves the way for other forced climate simulations for the past 2000 years by using the CESM.
Using the low-resolution (T31, equivalent to 3.75°×3.75°) version of the Community Earth System Model (CESM) from the National Center for Atmospheric Research (NCAR), a global climate simulation was carried out with fixed external forcing factors (1850 Common Era. (C.E.) conditions) for the past 2000 years. Based on the simulated results, spatio-temporal structures of surface air temperature, precipitation and internal variability, such as the El Niño-Southern Oscillation (ENSO), the Atlantic Multi-decadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO), and the North Atlantic Oscillation (NAO), were compared with reanalysis datasets to evaluate the model performance. The results are as follows:1) CESM showed a good performance in the long-term simulation and no significant climate drift over the past 2000 years;2) climatological patterns of global and regional climate changes simulated by the CESM were reasonable compared with the reanalysis datasets;and 3) the CESM simulated internal natural variability of the climate system performs very well. The model not only reproduced the periodicity of ENSO, AMO and PDO events but also the 3-8 years variability of the ENSO. The spatial distribution of the CESM-simulated NAO was also similar to the observed. However, because of weaker total irradiation and greenhouse gas concentration forcing in the simulation than the present, the model performances had some differences from the observations. Generally, the CESM showed a good performance in simulating the global climate and internal natural variability of the climate system. This paves the way for other forced climate simulations for the past 2000 years by using the CESM.
2015, 25(3): 274-282.
doi: 10.1007/s11769-015-0748-z
Abstract:
Urban sprawl is threatening the limited highly fertile land in the Nile delta of Egypt. Landsat TM satellite images of 1984, 1992 and ETM+ of 2006 have been used to study the impact of urban sprawl on agricultural land of the Northern Nile delta, Egypt. Visual interpretation using on screen digitizing and change detection techniques were applied for monitoring the urban sprawl. Combining the land capability map and the urban thematic layer using GIS made it possible to point out the risk of urban expansion on the expense of the highly capable soil class. The results show that a total expansion of urban area amounted to 689.20 km2 (6.3% of total area) during the study period 1984-2006. The urban expansion during the 1984-2006 was on the expense of the most fertile soils where, the high capable soils (Class I) lost 247.14 km2 (2.26 % of total area) and the moderate capable soils lost 32.73 km2 (0.3% of total area), while the low capable soils lost only 57.39 km2 (0.53% of total area). The urban encroachment over the non capable soils was very limited during the study period 1984-1992, where 7.33 km2 only was lost. The pattern of urban sprawl has been changed during the 1992 to 2006 whereas much larger area (50.64 km2) of the non capable soils was converted to urban. It can be concluded that the urban sprawl is one of the dominant degradation process on the land of Nile Delta.
Urban sprawl is threatening the limited highly fertile land in the Nile delta of Egypt. Landsat TM satellite images of 1984, 1992 and ETM+ of 2006 have been used to study the impact of urban sprawl on agricultural land of the Northern Nile delta, Egypt. Visual interpretation using on screen digitizing and change detection techniques were applied for monitoring the urban sprawl. Combining the land capability map and the urban thematic layer using GIS made it possible to point out the risk of urban expansion on the expense of the highly capable soil class. The results show that a total expansion of urban area amounted to 689.20 km2 (6.3% of total area) during the study period 1984-2006. The urban expansion during the 1984-2006 was on the expense of the most fertile soils where, the high capable soils (Class I) lost 247.14 km2 (2.26 % of total area) and the moderate capable soils lost 32.73 km2 (0.3% of total area), while the low capable soils lost only 57.39 km2 (0.53% of total area). The urban encroachment over the non capable soils was very limited during the study period 1984-1992, where 7.33 km2 only was lost. The pattern of urban sprawl has been changed during the 1992 to 2006 whereas much larger area (50.64 km2) of the non capable soils was converted to urban. It can be concluded that the urban sprawl is one of the dominant degradation process on the land of Nile Delta.
2015, 25(3): 283-294.
doi: 10.1007/s11769-015-0740-7
Abstract:
Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe (Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30-40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Indices (NDVIs) and land surface reflectance data from Landsat Thematic Mapper (TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient (CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30-40 cm depth in the study area (with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.
Spatial distribution of soil salinity can be estimated based on its environmental factors because soil salinity is strongly affected and indicated by environmental factors. Different with other properties such as soil texture, soil salinity varies with short-term time. Thus, how to choose powerful environmental predictors is especially important for soil salinity. This paper presents a similarity-based prediction approach to map soil salinity and detects powerful environmental predictors for the Huanghe (Yellow) River Delta area in China. The similarity-based approach predicts the soil salinities of unsampled locations based on the environmental similarity between unsampled and sampled locations. A dataset of 92 points with salt data at depth of 30-40 cm was divided into two subsets for prediction and validation. Topographical parameters, soil textures, distances to irrigation channels and to the coastline, land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Indices (NDVIs) and land surface reflectance data from Landsat Thematic Mapper (TM) imagery were generated. The similarity-based prediction approach was applied on several combinations of different environmental factors. Based on three evaluation indices including the correlation coefficient (CC) between observed and predicted values, the mean absolute error and the root mean squared error we found that elevation, distance to irrigation channels, soil texture, night land surface temperature, NDVI, and land surface reflectance Band 5 are the optimal combination for mapping soil salinity at the 30-40 cm depth in the study area (with a CC value of 0.69 and a root mean squared error value of 0.38). Our results indicated that the similarity-based prediction approach could be a vital alternative to other methods for mapping soil salinity, especially for area with limited observation data and could be used to monitor soil salinity distributions in the future.
2015, 25(3): 295-308.
doi: 10.1007/s11769-014-0690-5
Abstract:
This study examined the spatiotemporal dynamics of colored dissolved organic matter (CDOM) and spectral slope (S), and further to analyze its sources in three productive water supplies (Eagle Creek, Geist and Morse reservoirs) from Indiana, USA. The results showed that he absorption coefficient aCDOM(440) ranged from 0.37 m-1 to 3.93 m-1 with an average of 1.89±0.76 m-1 (±SD) for the aggregated dataset, and S varied from 0.0048 nm-1 to 0.0239 nm-1 with an average of 0.0108±0.0040 nm-1. A significant relationship between S and aCDOM(440) can be fitted with a power equation (S=0.013×aCDOM(440)-0.42, R2=0.612), excluding data from Geist Reservoir during high flow (12 April 2010) and the Morse Reservoir on 25 June 2010 due to a T-storm achieves even higher determination coefficient (R2=0.842). Correlation analysis indicated that aCDOM(440) has strong association with inorganic suspended matter (ISM) concentration (0.231 < R2< 0.786) for each of the field surveys, and this trend followed the aggregated datasets (R2=0.447, p< 0.001). In contrast, chlorophyll-a was only correlated with aCDOM(440) in summer and autumn (0.081 < R2< 0.763), indicating that CDOM is mainly from terrigenous sources in early spring and that phytoplankton contributed during the algal blooming season. The S value was used to characterize CDOM origin. The results indicate that the CDOM source is mainly controlled by hydrological variations, while phytoplankton originated organic matter also closely linked with CDOM dynamics in three productive reservoirs.
This study examined the spatiotemporal dynamics of colored dissolved organic matter (CDOM) and spectral slope (S), and further to analyze its sources in three productive water supplies (Eagle Creek, Geist and Morse reservoirs) from Indiana, USA. The results showed that he absorption coefficient aCDOM(440) ranged from 0.37 m-1 to 3.93 m-1 with an average of 1.89±0.76 m-1 (±SD) for the aggregated dataset, and S varied from 0.0048 nm-1 to 0.0239 nm-1 with an average of 0.0108±0.0040 nm-1. A significant relationship between S and aCDOM(440) can be fitted with a power equation (S=0.013×aCDOM(440)-0.42, R2=0.612), excluding data from Geist Reservoir during high flow (12 April 2010) and the Morse Reservoir on 25 June 2010 due to a T-storm achieves even higher determination coefficient (R2=0.842). Correlation analysis indicated that aCDOM(440) has strong association with inorganic suspended matter (ISM) concentration (0.231 < R2< 0.786) for each of the field surveys, and this trend followed the aggregated datasets (R2=0.447, p< 0.001). In contrast, chlorophyll-a was only correlated with aCDOM(440) in summer and autumn (0.081 < R2< 0.763), indicating that CDOM is mainly from terrigenous sources in early spring and that phytoplankton contributed during the algal blooming season. The S value was used to characterize CDOM origin. The results indicate that the CDOM source is mainly controlled by hydrological variations, while phytoplankton originated organic matter also closely linked with CDOM dynamics in three productive reservoirs.
2015, 25(3): 309-320.
doi: 10.1007/s11769-015-0735-4
Abstract:
Wetlands are sensitive to climate change, in the same time, wetlands can influence climate. This study analyzed the spa-tio-temporal characteristics of wetland change in the semi-arid zone of Northeast China from 1985 to 2010, and investigated the impact of large area of wetland change on local climate. Results showed that the total area of wetlands was on a rise in the study area. Although natural wetlands (marshes, riparians and lakes) decreased, constructed wetlands (rice fields) increased significantly, and the highest increase rate in many places exceeded 30%. Anthropogenic activities are major driving factors for wetland change. Wetland change produced an impact on local climate, mainly on maximum temperature and precipitation during the period of May-September. The increase (or decrease) of wetland area could reduce (or increase) the increment of maximum temperature and the decrement of precipitation. The changes in both maximum temperature and precipitation corresponded with wetland change in spatial distribution. Wetland change played a more important role in moderating local climate compared to the contribution of woodland and grassland changes in the study area. Cold-humid effect of wetlands was main way to moderating local climate as well as alleviating climatic warming and drying in the study area, and heterogeneity of underlying surface broadened the cold-humid effect of wetlands.
Wetlands are sensitive to climate change, in the same time, wetlands can influence climate. This study analyzed the spa-tio-temporal characteristics of wetland change in the semi-arid zone of Northeast China from 1985 to 2010, and investigated the impact of large area of wetland change on local climate. Results showed that the total area of wetlands was on a rise in the study area. Although natural wetlands (marshes, riparians and lakes) decreased, constructed wetlands (rice fields) increased significantly, and the highest increase rate in many places exceeded 30%. Anthropogenic activities are major driving factors for wetland change. Wetland change produced an impact on local climate, mainly on maximum temperature and precipitation during the period of May-September. The increase (or decrease) of wetland area could reduce (or increase) the increment of maximum temperature and the decrement of precipitation. The changes in both maximum temperature and precipitation corresponded with wetland change in spatial distribution. Wetland change played a more important role in moderating local climate compared to the contribution of woodland and grassland changes in the study area. Cold-humid effect of wetlands was main way to moderating local climate as well as alleviating climatic warming and drying in the study area, and heterogeneity of underlying surface broadened the cold-humid effect of wetlands.
2015, 25(3): 321-336.
doi: 10.1007/s11769-014-0718-x
Abstract:
Information on the spatial distribution of soil salinity can be used as guidance in avoiding the continued degradation of land and water resources by better informing policy makers. However, most regional soil-salinity maps are produced through a conventional direct-linking method derived from historic observations. Such maps lack spatial details and are limited in describing the evolution of soil salinization in particular instances. To overcome these limitations, we employed a method that included an integrative hierarchical-sampling strategy (IHSS) and the Soil Land Inference Model (SoLIM) to map soil salinity over a regional area. A fuzzy c-means (FCM) classifier is performed to generate three measures, comprising representative grade, representative area, and representative level (membership). IHSS employs these three measures to ascertain how many representative samples are appropriate. Through this synergetic assessment, representative samples are obtained and their soil-salinity values are measured. These samples are input to SoLIM, which is constructed based on fuzzy logic, to calculate the soil-forming environmental similarities between representative samples and other locations. Finally, a detailed soil-salinity map is produced through an averaging function that is linearly weighted, which is used to integrate the soil salinity value and soil similarity. This case study, in the Uyghur Autonomous Region of Xinjiang of China, demonstrates that the employed method can produce soil salinity map at a higher level of spatial detail and accuracy. Twenty-three representative points are determined. The results show that 1) the prediction is appropriate in Kuqa Oasis (R2=0.70, RPD=1.55, RMSE=12.86) and Keriya Oasis (R2=0.75, RPD=1.66, RMSE=10.92), that in Fubei Oasis (R2=0.77, RPD=2.01, RMSE=6.32) perform little better than in those two oases, according to the evaluation criterion. 2) Based on all validation samples from three oases, accuracy estimation show the employed method (R2=0.74, RPD=1.67, RMSE=11.18) performed better than the multiple linear regression model (R2=0.60, RPD=1.47, RMSE=14.45). 3) The statistical result show that approximately half (48.07%) of the study area has changed to salt-affected soil, mainly distributed in downstream of oases, around lakes, on both sides of rivers and more serious in the southern than the northern Xinjiang. To deal with this issue, a couple of strategies involving soil-salinity monitoring, water management, and plant diversification are proposed, to reduce soil salinization. Finally, this study concludes that the employed method can serve as an alternative model for soil-salinity mapping on a large scale.
Information on the spatial distribution of soil salinity can be used as guidance in avoiding the continued degradation of land and water resources by better informing policy makers. However, most regional soil-salinity maps are produced through a conventional direct-linking method derived from historic observations. Such maps lack spatial details and are limited in describing the evolution of soil salinization in particular instances. To overcome these limitations, we employed a method that included an integrative hierarchical-sampling strategy (IHSS) and the Soil Land Inference Model (SoLIM) to map soil salinity over a regional area. A fuzzy c-means (FCM) classifier is performed to generate three measures, comprising representative grade, representative area, and representative level (membership). IHSS employs these three measures to ascertain how many representative samples are appropriate. Through this synergetic assessment, representative samples are obtained and their soil-salinity values are measured. These samples are input to SoLIM, which is constructed based on fuzzy logic, to calculate the soil-forming environmental similarities between representative samples and other locations. Finally, a detailed soil-salinity map is produced through an averaging function that is linearly weighted, which is used to integrate the soil salinity value and soil similarity. This case study, in the Uyghur Autonomous Region of Xinjiang of China, demonstrates that the employed method can produce soil salinity map at a higher level of spatial detail and accuracy. Twenty-three representative points are determined. The results show that 1) the prediction is appropriate in Kuqa Oasis (R2=0.70, RPD=1.55, RMSE=12.86) and Keriya Oasis (R2=0.75, RPD=1.66, RMSE=10.92), that in Fubei Oasis (R2=0.77, RPD=2.01, RMSE=6.32) perform little better than in those two oases, according to the evaluation criterion. 2) Based on all validation samples from three oases, accuracy estimation show the employed method (R2=0.74, RPD=1.67, RMSE=11.18) performed better than the multiple linear regression model (R2=0.60, RPD=1.47, RMSE=14.45). 3) The statistical result show that approximately half (48.07%) of the study area has changed to salt-affected soil, mainly distributed in downstream of oases, around lakes, on both sides of rivers and more serious in the southern than the northern Xinjiang. To deal with this issue, a couple of strategies involving soil-salinity monitoring, water management, and plant diversification are proposed, to reduce soil salinization. Finally, this study concludes that the employed method can serve as an alternative model for soil-salinity mapping on a large scale.
2015, 25(3): 337-349.
doi: 10.1007/s11769-014-0712-3
Abstract:
Cultivated land transition and its driving mechanism are the hotspots among studies on land use change. In this study, we constructed a framework to study the driving mechanism of cultivated land transition from the quantitative perspective. Based on the vector data of land use in 1990, 2000 and 2009 of Yantai Proper, Shandong Province China, 11 explanatory variables were chosen from two aspects:the elevation, slope, cost distance to major water area and cost distance to minor water area, which presented physical factors;cost distance to district center, cost distance to town center, cost distance to city expansion center, cost distance to major roads, cost distance to city roads, cost distance to county roads and cost distance to rural roads, which presented the socio-economic factors. Combined with spatial analysis tools and Logistic regression analysis model, we construct Logistic regression analyses with four objectives that were urban construction land, rural residential land, orchard and other lands. The results show that, cost distance to district center, cost distance to town center, cost distance to city expansion center and cost distance to city roads are the significant explanatory variables for the transition of cultivated land into urban construction land. The main explained factors on the transition of cultivated land into rural residential land are slope, cost distance to town center, cost distance to county roads and cost distance to rural roads. Slope, cost distance to minor water area, cost distance to town center, cost distance to county roads and cost distance to rural roads are the significant explanatory variables for the transition of cultivated land into orchard land. Elevation, slope, cost distance to major water area and cost distance to minor water area are the main explanatory variables on the transition of cultivated land into other land uses.
Cultivated land transition and its driving mechanism are the hotspots among studies on land use change. In this study, we constructed a framework to study the driving mechanism of cultivated land transition from the quantitative perspective. Based on the vector data of land use in 1990, 2000 and 2009 of Yantai Proper, Shandong Province China, 11 explanatory variables were chosen from two aspects:the elevation, slope, cost distance to major water area and cost distance to minor water area, which presented physical factors;cost distance to district center, cost distance to town center, cost distance to city expansion center, cost distance to major roads, cost distance to city roads, cost distance to county roads and cost distance to rural roads, which presented the socio-economic factors. Combined with spatial analysis tools and Logistic regression analysis model, we construct Logistic regression analyses with four objectives that were urban construction land, rural residential land, orchard and other lands. The results show that, cost distance to district center, cost distance to town center, cost distance to city expansion center and cost distance to city roads are the significant explanatory variables for the transition of cultivated land into urban construction land. The main explained factors on the transition of cultivated land into rural residential land are slope, cost distance to town center, cost distance to county roads and cost distance to rural roads. Slope, cost distance to minor water area, cost distance to town center, cost distance to county roads and cost distance to rural roads are the significant explanatory variables for the transition of cultivated land into orchard land. Elevation, slope, cost distance to major water area and cost distance to minor water area are the main explanatory variables on the transition of cultivated land into other land uses.
2015, 25(3): 350-360.
doi: 10.1007/s11769-014-0729-7
Abstract:
Based on the logical causal relationship and taking Liaoning Province, China, which is the Chinese traditional industrial base and is in the stage of accelerated urbanisation, as a case study, this study builds the 'Urbanisation-Energy Consumption-CO2 Emissions System Dynamics (UEC-SD)' model using a system dynamics method. The UEC-SD model is applied to analyse the effect of the urbanisation process on the regional energy structure and CO2 emissions, followed by simulation of future production and living energy consumption structure as well as the evolutionary trend of CO2 emissions of three urbanisation scenarios (low speed, intermediate speed and high speed) under the assumed boundary conditions in urban and rural areas of Liaoning Province, China. The results show that the urbanisation process can alter production and the living energy consumption structure and thereby change regional CO2 emissions. An increase in the urbanisation rate in case area will lead to regional CO2 emissions rising in the short term, but when the urbanisation rate approaches 80%, CO2 emissions will reach a peak value and then decrease. Comparison of different urbanisation rates showed that production and living energy consumption exhibit different directions of change and rules in urban and rural areas. The effect of urbanisation on CO2 emissions and energy structure is not direct, and urbanisation can increase the differences in energy and CO2 emissions between urban and rural areas caused by the industrial structure, technical level and other factors.
Based on the logical causal relationship and taking Liaoning Province, China, which is the Chinese traditional industrial base and is in the stage of accelerated urbanisation, as a case study, this study builds the 'Urbanisation-Energy Consumption-CO2 Emissions System Dynamics (UEC-SD)' model using a system dynamics method. The UEC-SD model is applied to analyse the effect of the urbanisation process on the regional energy structure and CO2 emissions, followed by simulation of future production and living energy consumption structure as well as the evolutionary trend of CO2 emissions of three urbanisation scenarios (low speed, intermediate speed and high speed) under the assumed boundary conditions in urban and rural areas of Liaoning Province, China. The results show that the urbanisation process can alter production and the living energy consumption structure and thereby change regional CO2 emissions. An increase in the urbanisation rate in case area will lead to regional CO2 emissions rising in the short term, but when the urbanisation rate approaches 80%, CO2 emissions will reach a peak value and then decrease. Comparison of different urbanisation rates showed that production and living energy consumption exhibit different directions of change and rules in urban and rural areas. The effect of urbanisation on CO2 emissions and energy structure is not direct, and urbanisation can increase the differences in energy and CO2 emissions between urban and rural areas caused by the industrial structure, technical level and other factors.
2015, 25(3): 361-374.
doi: 10.1007/s11769-015-0755-0
Abstract:
Sustainable development of mountain areas and resource cities has been a significant issue worldwide. Transformation of mountain resource cities is facing tremendous difficulties. In the context of National Major Function-oriented Zone Planning raised to a national strategic level in China, it is important to effectively implement the planning by studying geographical space development zone classification of prefecture- and county-level cities based on their major function orientation. This research is even more critical for the transformation of mountain resource cities. In this study, we evaluated geographical space development suitability, and classified geo-graphical space development zones for Dongchuan Distric in Kunming City of Yunan Province, China, a typical mountain resource city. A quantitative grid-based evaluation was conducted using key-factor identification and restrictive-supportive comprehensive index determinati based on a geographic information system framework with different source data. The results included a classification of geographical space of Dongchuan into five types:the prohibited development zone, the inappropriate development zone, the controlled development zone, the moderate development zone and the preferential development zone. The distribution characteristics of geographical space development zones showed that the proportion of the prohibited development zone is the highest, while that of the other four development zones is comparatively lower and significantly fragmented. The geographical space development suitability is greatly controlled by the geomorphic pattern. Although Dongchuan is extremely restrained in terms of suitable geographical space for industrialization and urbanization, it still has the certain preferential and moderate development zones with an area of 207.81 km2 with large parts being concentrated and contiguous, which makes these areas the most favorable for development. Only by adapting to this reality and implementing centralized development strategy in the regions with higher suitability may Dongchuan achieve smooth transformation from expansion mode to compact mode and its sustainable development capacity may be improved. Suggestions with an angle of industrial and spatial development pattern were put forward for the transformation of the city in future.
Sustainable development of mountain areas and resource cities has been a significant issue worldwide. Transformation of mountain resource cities is facing tremendous difficulties. In the context of National Major Function-oriented Zone Planning raised to a national strategic level in China, it is important to effectively implement the planning by studying geographical space development zone classification of prefecture- and county-level cities based on their major function orientation. This research is even more critical for the transformation of mountain resource cities. In this study, we evaluated geographical space development suitability, and classified geo-graphical space development zones for Dongchuan Distric in Kunming City of Yunan Province, China, a typical mountain resource city. A quantitative grid-based evaluation was conducted using key-factor identification and restrictive-supportive comprehensive index determinati based on a geographic information system framework with different source data. The results included a classification of geographical space of Dongchuan into five types:the prohibited development zone, the inappropriate development zone, the controlled development zone, the moderate development zone and the preferential development zone. The distribution characteristics of geographical space development zones showed that the proportion of the prohibited development zone is the highest, while that of the other four development zones is comparatively lower and significantly fragmented. The geographical space development suitability is greatly controlled by the geomorphic pattern. Although Dongchuan is extremely restrained in terms of suitable geographical space for industrialization and urbanization, it still has the certain preferential and moderate development zones with an area of 207.81 km2 with large parts being concentrated and contiguous, which makes these areas the most favorable for development. Only by adapting to this reality and implementing centralized development strategy in the regions with higher suitability may Dongchuan achieve smooth transformation from expansion mode to compact mode and its sustainable development capacity may be improved. Suggestions with an angle of industrial and spatial development pattern were put forward for the transformation of the city in future.
2015, 25(3): 375-388.
doi: 10.1007/s11769-015-0738-1
Abstract:
With the urban expansion and economic restructuring, the jobs-housing relationship has become an important issue in studies on urban spatial structure. This paper employed a job accessibility model, which is an evaluation instrument to measure the jobs-housing relationship in Beijing Metropolitan Area from a job accessibility perspective. The results indicate that the population in the central city is declining, whereas the population in the suburbs is consistently growing and forming new population centers. However, the distribution pattern of employment is still highly centralized. Job accessibility varies in different locations, but the inner-city areas (within the Third Ring road) have seen improved job accessibility over time while job accessibility in the suburbs (especially outside the Fourth Ring road) has decreased, and this has led it to become a primary area of residential and employment mismatch. At the same time, the new towns in the outer suburbs have not yet demonstrated great potential to attract more jobs. In addition we find that, to some extent, urban planning changes the jobs-housing relationship, but a polycentric urban spatial structure is not yet evident. The floating population and related housing policy also affect the jobs-housing relationship. We propose some measures to resolve the spatial mismatch as well as some future research directions.
With the urban expansion and economic restructuring, the jobs-housing relationship has become an important issue in studies on urban spatial structure. This paper employed a job accessibility model, which is an evaluation instrument to measure the jobs-housing relationship in Beijing Metropolitan Area from a job accessibility perspective. The results indicate that the population in the central city is declining, whereas the population in the suburbs is consistently growing and forming new population centers. However, the distribution pattern of employment is still highly centralized. Job accessibility varies in different locations, but the inner-city areas (within the Third Ring road) have seen improved job accessibility over time while job accessibility in the suburbs (especially outside the Fourth Ring road) has decreased, and this has led it to become a primary area of residential and employment mismatch. At the same time, the new towns in the outer suburbs have not yet demonstrated great potential to attract more jobs. In addition we find that, to some extent, urban planning changes the jobs-housing relationship, but a polycentric urban spatial structure is not yet evident. The floating population and related housing policy also affect the jobs-housing relationship. We propose some measures to resolve the spatial mismatch as well as some future research directions.
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