Classifying and mapping the urban transition in Vietnam

The urban transition almost always involves wrenching social adjustment as small agricultural communities are forced to adjust rapidly to industrial ways of life. Large-scale in-migration of young people, usually from poor regions, creates enormous demand and expectations for community and social services. One immediate problem planners face in approaching this challenge is how to define, differentiate, and map what is rural, urban, and transitional (i.e., peri-urban). This project established an urban classification for Vietnam by using national census and remote sensing data to identify and map the smallest administrative units for which data are collected as rural, peri-urban, urban, or urban core. We used both natural and human factors in the quantitative model: income from agriculture, land under agriculture and forests, houses with modern sanitation, and the Normalized Difference Vegetation Index. Model results suggest that in 2006, 71% of Vietnam's 10,891 communes were rural, 18% peri-urban, 3% urban, and 4% urban core. Of the communes our model classified as peri-urban, 61% were classified by the Vietnamese government as rural. More than 7% of Vietnam's land area can be classified as peri-urban and approximately 13% of its population (more than 11 million people) lives in peri-urban areas. We identified and mapped three types of peri-urban places: communes in the periphery of large towns and cities; communes along highways; and communes associated with provincial administration or home to industrial, energy, or natural resources projects (e.g., mining). We validated this classification based on ground observations, analyses of multi-temporal night-time lights data, and an examination of road networks. The model provides a method for rapidly assessing the rural–urban nature of places to assist planners in identifying rural areas undergoing rapid change with accompanying needs for investments in building, sanitation, road infrastructure, and government institutions.     

Spatial structure of central places in Jilin Central Urban Agglomeration,Jilin Province,China

Using the radius of gyration from fractal theory, this paper describes the calculation of fractal dimensions for the four tiers of central places in the Jilin Central Urban Agglomeration(JCUA), Jilin Province, China and the structural characteristics of each tier: 1) the 1st tier central place, Changchun Proper(not including Shuangyang District), provides the most service functions and has the most stable primate position; 2) the 2nd tier central places, Jilin Proper, Siping Proper, Liaoyuan Proper and Songyuan Proper have unclear statuses and do not provide certain functions; 3) the 3rd tier central places comprise 23 county-level cities, counties and urban districts(including Shuangyang District of Changchun), exhibiting a dense spatial structure that agrees with theory; 4) the 4th tier contains the largest number of central places(248 designated towns), but they are loosely distributed. In this study, a spatial image of the JCUA was created, based on vectorized data of the urban settlement distribution, which was then modified and abstracted to create a hexagonal network covering the JCUA. Compared to the traditional central place model, the modified spatial image conforms to the K = 3 principle. In reality, however, the growth of some 3rd tier central places should be increased with the cities being upgraded to the 2nd tier so as to overcome that tier′s functional deficiency. The loose distribution in the 4th tier should also be changed. This apparent anomaly can be explained by the fact that the classic hexagon model used to describe the way market areas layout does not exist in the real world. However, this should not be viewed as an obstacle to using central place theory. If its assumptions are properly applied, it can still assist research into the spatial structure of regions.     

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Many municipal activities require updated large-scale maps that include both topographic and thematic information. For this purpose, the efficient use of very high spatial resolution (VHR) satellite imagery suggests the development of approaches that enable a timely discrimination, counting and delineation of urban elements according to legal technical specifications and quality standards. Therefore, the nature of this data source and expanding range of applications calls for objective methods and quantitative metrics to assess the quality of the extracted information which go beyond traditional thematic accuracy alone. The present work concerns the development and testing of a new approach for using technical mapping standards in the quality assessment of buildings automatically extracted from VHR satellite imagery. Feature extraction software was employed to map buildings present in a pansharpened QuickBird image of Lisbon. Quality assessment was exhaustive and involved comparisons of extracted features against a reference data set, introducing cartographic constraints from scales 1:1000, 1:5000, and 1:10,000. The spatial data quality elements subject to evaluation were: thematic (attribute) accuracy, completeness, and geometric quality assessed based on planimetric deviation from the reference map. Tests were developed and metrics analyzed considering thresholds and standards for the large mapping scales most frequently used by municipalities. Results show that values for completeness varied with mapping scales and were only slightly superior for scale 1:10,000. Concerning the geometric quality, a large percentage of extracted features met the strict topographic standards of planimetric deviation for scale 1:10,000, while no buildings were compliant with the specification for scale 1:1000.     

Effects of green space spatial pattern on land surface temperature: Implications for sustainable urban planning and climate change adaptation

The urban heat island (UHI) refers to the phenomenon of higher atmospheric and surface temperatures occurring in urban areas than in the surrounding rural areas. Mitigation of the UHI effects via the configuration of green spaces and sustainable design of urban environments has become an issue of increasing concern under changing climate. In this paper, the effects of the composition and configuration of green space on land surface temperatures (LST) were explored using landscape metrics including percentage of landscape (PLAND), edge density (ED) and patch density (PD). An oasis city of Aksu in Northwestern China was used as a case study. The metrics were calculated by moving window method based on a green space map derived from Landsat Thematic Mapper (TM) imagery, and LST data were retrieved from Landsat TM thermal band. A normalized mutual information measure was employed to investigate the relationship between LST and the spatial pattern of green space. The results showed that while the PLAND is the most important variable that elicits LST dynamics, spatial configuration of green space also has significant effect on LST. Though, the highest normalized mutual information measure was with the PLAND (0.71), it was found that the ED and PD combination is the most deterministic factors of LST than the unique effects of a single variable or the joint effects of PLAND and PD or PLAND and ED. Normalized mutual information measure estimations between LST and PLAND and ED, PLAND and PD and ED and PD were 0.7679, 0.7650 and 0.7832, respectively. A combination of the three factors PLAND, PD and ED explained much of the variance of LST with a normalized mutual information measure of 0.8694. Results from this study can expand our understanding of the relationship between LST and street trees and vegetation, and provide insights for sustainable urban planning and management under changing climate.     

Outstanding challenges for urban conservation research and action

Researchers, advocates and policymakers have proposed urban conservation as an emerging, integrative discipline that can contribute to sustainable cities by delivering co-benefits to human and non-human components of biodiversity. Given the recent growth in biodiversity-friendly designs and management schemes, there is an urgent need for a synthesis of this fragmented research base to inform planners and decision-makers. We conducted a systematic multidisciplinary literature review (787 papers) and found that the importance of urban areas for general conservation is not convincingly supported by empirical research. Only few studies demonstrated that cities can directly contribute to conservation efforts, by hosting viable populations of rare or endangered species, or by providing green corridors for the passage of natural populations. From a social perspective, while several studies demonstrated that green infrastructure could provide services for people (notably cultural services), only few studies explored the role of species diversity per se. Our review also shows strong geographical, location and taxonomic biases in urban biodiversity conservation research that make generalisations difficult. It is a disturbing paradox that while research in urban biodiversity conservation is rising exponentially, the main motivations for conserving urban biodiversity remain largely untested and unproven. We thus propose a framework for promoting integrative urban conservation research to bridge those gaps. Together, these findings warn against expanding cities under green planning and call for enhancing biodiversity experience by improving the quality of existing green spaces throughout the entire urban matrix. We provide a set of recommendations for practitioners and decision-makers to continue action.     

基于人工蜂群CA模型的南京市土地利用变化模拟与预测

以南京市为例,构建人工蜂群元胞自动机（CA_ABC）模型,对2000—2007年的土地利用变化进行模拟以实现CA_ABC模型的校正,并以2007—2015年的土地利用变化为案例,验证该模型的有效性。模拟结果总体精度（OA）2007年为87.79%,2015年为80.61%;模拟结果的品质因数（FOM）2007年为21.23%,2015年为19.25%。基于CA_ABC模型和马尔可夫链预测未来城市土地总量,对南京市2025和2035年的土地利用格局进行了预测,对城市扩张和生态用地被侵占现象进行分析。模型预测结果表明,未来20年的城市扩张主要以牺牲耕地和林地为代价,2025和2035年80%的城市扩张面积来源于对耕地面积的侵占,17%的城市面积扩张是由2015年的林地转换得到的。研究表明,准确模拟、预测未来城市格局及评估城市扩张能够对生态用地侵占,以及为决策者合理规划城市、推动城市可持续发展提供帮助。     

基于腹地划分的中国城市群空间影响范围识别

城市群空间影响范围的界定是认清城市群内中心城市与腹地、城市与城市间相互关联的基础。采用主成分分析法计算中国287个地级及以上城市的结节性指数,利用累积耗费距离法和k阶数据场,综合测度了交通可达性与空间场能,根据场强"取大"原则对城市腹地进行了划分,依据城市腹地范围界定了城市群的空间影响范围。结果表明:①中国地级以上城市最大和最小结节性指数相差157.53倍,城市平均可达时间为193.43 min,场能平均值为5 412,最大和最小城市腹地相差3 751倍;②23个城市群中,空间影响范围最大的是北疆城市群,空间平均场强最大的是中原城市群;③城市群在空间上呈现为发育程度不同的"11+9+3"的分布格局和逆时针90°旋转的"β"型城市群连绵带发展格局;④基于腹地划分的城市群空间影响范围充分考虑了现实的交通网络和地形地貌特点,实现了对广域空间范围内城市腹地范围和城市群空间影响范围的定量测度。     

苏州城市规划区地下空间开发适宜性评价

结合苏州城市规划区的地质环境特点,选取地形地貌、建筑场地类别、不良岩土体、水文条件、地质灾害等5个方面13个因子作为地下空间资源开发适宜性评价指标,运用层次分析法(AHP)确定各指标因子的权重,采用多目标线性加权函数建立评价数学模型,利用GIS叠加生成适宜性评价图。结果表明:西部基岩山区地质环境条件较好,适宜地下空间开发,东部平原区水系发育,软土层较厚,水文条件复杂,地下空间开发适宜性相对较差。     

基于主体功能区的京津冀城市群碳收支时空分异与碳补偿分区

从主体功能区划视角研究碳收支和碳补偿分区,对于制定适应各主体功能区低碳发展策略,推动区域生态环境协同治理,实现高质量发展具有重要意义,也是地理学思想对于实现“双碳”目标的重要贡献。本文首先构建主体功能区视角下碳收支与碳补偿的理论框架,然后以京津冀城市群157个县级单元为研究区,引入集中化指数、标准显性比较优势指数、SOM-K-means聚类等方法研究功能区视角下京津冀城市群碳收支时空分异与碳补偿分区,并提出以低碳发展为导向的碳减排空间优化方案。结果表明：① 2000—2017年京津冀城市群碳收支量呈现波动上升态势,其集中化指数均高于0.4的“警戒线”,碳收支地区差异整体偏大。② 碳收支时空分异显著,碳排放高值区呈现以京津唐为中心,向外逐步降低的“核心—外围”空间格局;而碳吸收空间格局趋于稳定,总体呈现东、北、西部高,而中、南部低的倒“U”型格局。③ 京津冀城市群碳收支与主体功能区战略定位较为吻合,优化开发区和重点开发区是碳排放的主要承压区,而重点生态功能区是碳吸收的优势主导区,各功能区碳吸收集中化指数差别较碳排放集中化指数小。④ 京津冀城市群共有53个支付区、64个平衡区和40个获补区,结合主体功能区规划战略目标,最终形成9类碳补偿空间优化区,并提出每一类型区低碳发展方向及策略。⑤ 未来要加强更微观尺度的碳收支及碳补偿研究,丰富和完善碳补偿理论框架,将碳补偿融入到碳交易市场,探索实现“双碳”目标的多元化路径。     

基于城市创新力的长三角城市群高学历流动人口空间溢出效应研究

高学历流动人口作为城市创新隐性主体,是城市发展不可忽略的一股新生力量。选取2014—2018年长三角城市群26个城市中具有普通高等及以上学历的流动人口等相关数据,运用相关数理和空间模型探究高学历流动人口与城市创新能力的空间关联特征及空间溢出效应。结果表明:在长三角城市群26个城市中,高学历流动人口规模与城市创新能力呈现以上海为核心向周边城市等级扩散的“核心-边缘”格局;高学历流动人口具有高度的空间自相关性并对城市创新能力产生明显的影响,且区域创新格局复杂,两者在局部空间的集聚模式存在差异;高学历流动人口对城市创新能力存在显著的正向空间效应,且间接效应强于直接效应。