Theory and measurement model of administrative region potential from a perspective of administrative division adjustment: Taking Chongqing city as a case study

Journal of Geographical Sciences - There is a lack of basic theory and method to examine the effect of administrative division (AD) adjustment on the regional development. Based on the theory and...     

Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.     

Integration of remote sensing,county-level census,and machine learning for century-long regional cropland distribution data reconstruction

The Lower Mississippi Alluvial Valley (LMAV) was home to about ten million hectare bottomland hardwood (BLH) forests in the Southern U.S. It experienced over 80 % area loss of the BLH forests in the past centuries and large-scale afforestation in recent decades. Due to the lack of a high-resolution cropland dataset, impacts of land use change (LUC) on the LMAV ecosystem services have not been fully understood. In this study, we developed a novel framework by integrating the machine learning algorithm, county-level agricultural census, and satellite-based cropland products to reconstruct the LMAV cropland distribution during 1850–2018 at a 30-m resolution. Results showed that the LMAV cropland area increased from 0.78 × 10⁴ km² in 1850 to 6.64 × 10⁴ km² in 1980 and then decreased to 6.16 × 10⁴ km² in 2018. Cropland expansion rate was the largest in the 1960s (749 km² yr⁻¹) but decreased rapidly thereafter, whereas cropland abandonment rate increased substantially in recent decades with the largest rate of 514 km² yr⁻¹ in the 2010s. Our dataset has three notable features: (1) the depiction of fine spatial details, (2) the integration of the county-level census, and (3) the inclusion of a machine-learning algorithm trained by satellite-based land cover product. Most importantly, our dataset well captured the continuous increasing trend in cropland area from 1930–1960, which was misrepresented by other cropland datasets reconstructed from the state-level census. Our dataset would be important to accurately evaluate the impacts of historical deforestation and recent afforestation efforts on regional ecosystem services, attribute the observed hydrological changes to anthropogenic and natural driving factors, and investigate how the socioeconomic factors control regional LUC pattern. Our framework and dataset are crucial to developing managerial and policy strategies for conserving natural resources and enhancing ecosystem services in the LMAV.     

The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity

Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence.     

融合多源时空数据的城市火灾危险性评估

城市层面的火灾风险评估主要包括火灾危险性、危害性及救援能力等方面。本文选取火灾危险性评估进行针对性研究,在大数据思维的指导下,以相关关系代替因果关系,采用多源数据对评估指标权重、分值进行率定,得出福州市城区火灾危险性时空分布图。首先利用高德地图API对消防历史出警记录进行地址解析,将近万条火灾出警地址空间落点,获得福州市历史火灾空间分布;然后综合城市用地性质现状、用地开发性质、人口分布热力图等多源异构数据,探索其与历史火灾空间分布的相关性;最后以福州城区为例,初步实现具有充分数理支撑的火灾危险性评估方法,形成火灾危险性动态评估成果,为城市消防规划等提供支撑和依据。     

利用多元回归分析反演西南印度洋区域海底地形

针对海底地形与重力异常和重力异常垂直梯度在相应频段呈现强线性相关的特点,引入多元回归分析技术,提出并详细推导了联合多元重力数据的海底地形建模方法。然后,在西南印度洋SWIR(Southwest India Ridge)所在部分海域开展了海底地形反演试验及地形地貌分析研究。试验结果表明:6种海深模型中,基于多元回归分析技术构建的海深模型(BDVG模型)检核精度最高,相较于S&S V18.1模型和ETOPO1模型精度分别提高了11.51%和57.81%左右;2000 m以上水深海域,各个海深模型的检核精度较高,相对误差波动较小,反映了深海海域具有良好的反演效果;地形起伏剧烈海域或者浅海海域,BDVG海深模型,相较于以重力异常和重力异常垂直梯度作为单一输入源建立的BDG模型和BVGG模型相对误差及相对误差波动变化较小,反映了BDVG模型拥有更好的稳定性,从而体现了联合反演的必要性和优势。Indomed FZ—Gallieni FZ上唯一轴部缺失裂谷洋脊段(27洋脊段)目前属于岩浆供应充足阶段,构造作用的海底扩张对其影响较小;同时由于对称裂离方式影响,27洋脊段沿轴南北对称分布有地形隆起。     

建成环境对星级酒店内被盗的影响——以ZG市中心城区为例

盗窃是中西方酒店内财产犯罪中最为高发的一种,但鲜有研究关注建成环境对酒店被盗的影响。论文以2012—2014年ZG市中心城区发生过被盗的星级酒店作为研究样本,在综合分析酒店被盗时空特征的基础上,选取酒店周围500 m范围内的建成环境指标,利用负二项回归模型,对分时段各类型的星级酒店被盗的影响因素进行系统建模分析。结果表明：被盗星级酒店的时空分布呈现典型的集聚分布特征。不同时段空间“主热点”分布相对稳定,“次热点”有所不同。整体来看,提高服务水平对降低星级酒店被盗的作用最为稳定,周围的兴趣点(Point of Interest, POI)数量会显著增加星级酒店被盗的机会,道路交叉口则对星级酒店被盗起显著监管作用。分模型结果显示,服务水平对三星级和五星级酒店被盗的抑制作用显著,道路交叉口则对以商务客人为主的四星级酒店被盗的抑制作用更强,而POI数量对等级较低的三星、四星酒店被盗作用更为明显;大型零售商业中心能显著增加旅游旺季及周末时段的酒店被盗的数量,道路交叉口数量则对旅游淡季、工作日2个时段的星级酒店被盗风险的监控作用显著。研究表明建成环境在影响ZG市星级酒店被盗的机会和成本方面作用显著。结果验证了日常活动理论在中国大城市星级酒店被盗方面研究的适用性,拓展了犯罪地理学在星级酒店被盗方面的研究成果,对酒店盗窃预防有指导意义。     

海南省农村多维贫困及影响因素的空间分异

贫困具有多维属性,根据不同社会群体和背景从多维视角定义贫困已成为贫困问题研究的共识。依据Alkire-Foster多维贫困框架,拓展精准扶贫的“两不愁,三保障”识别标准,建立了涵盖教育、健康、居住、生活和收入指标的海南省农户多维贫困评估指标体系,基于海南省70个乡镇、134个贫困村3924户入户调查数据,采用双重临界值法评估了农户及村域多维贫困状况,进而运用地理加权回归(Geographically Weighted Regression,GWR)模型,分析了村域多维贫困影响因素的空间分异。结果显示,调查农户多维贫困率达18.22%,多维贫困程度严重的村多维贫困发生率不一定高,“两不愁、三保障”及收入指标对多维贫困指数的贡献率低。中、西部连片贫困地区多维贫困主要表现为较差的资产状况、不清洁的炊事燃料、较高的家庭成员患病率和较低的家庭成员最高学历。GWR模型分析表明,作为多维贫困最重要的影响因素,户主性别、户主受教育水平、女性劳动力占比和抚养比4个变量估计系数的空间分异明显。总体上,女性户主和低学历户主为主的地区倾向于更易发生多维贫困,二者的影响分别表现为从东到西、从北到南有所增强。女性劳动力占比为负向影响,抚养比为正向影响,呈现出自北向南增强的趋势,体现了海南贫困地区劳动力弱、女性相对更勤劳等典型地域特征。     

基于GWR模型的中国城市雾霾污染影响因素的空间异质性研究

基于全国城市的PM_2.5监测数据,识别PM_2.5的时空分布特征,并着重利用地理加权回归模型分析自然和社会经济因素对PM_2.5影响的空间异质性。结果显示：2015年全国PM_2.5的年均浓度为50.3 μg/m3,浓度变化呈现冬高夏低,春秋居中的“U型”特征;PM_2.5的空间集聚状态明显,其中京津冀城市群是全国PM_2.5的污染重心。地理加权回归结果显示：影响因素除高程外,其余指标均呈现正负两种效应,且影响程度具有显著的空间差异性特征。从回归系数的贡献均值来看,自然因素对城市PM_2.5浓度影响强度由高到低依次是高程、相对湿度、温度、降雨量、风速、植被覆盖指数;各类社会经济指标对城市PM_2.5浓度影响强度排名依次是人口密度、研发经费、建设用地比例、产业结构、外商直接投资、人均GDP。由于各指标对城市PM_2.5浓度变化的影响程度存在着空间异质性,因此在制定大气治理对策时可以考虑不同指标影响程度的空间差异,从而使得治霾对策更具针对性。     

广西土壤有机质空间变异特征及其影响因素研究

基于广西第二次土壤普查的270个土壤剖面资料,结合1∶50万数字化土壤类型图、土地利用类型图和气象监测数据等资料,利用地统计学和逐步回归分析等方法对广西表层土壤有机质空间变异特征及其影响因素进行了探究。结果表明：广西表层土壤有机质平均含量为3.11±2.19%,变异系数为70.72%,空间分布呈北高南低的趋势。广西表层土壤有机质空间分布受到自然和人为因素的共同影响,土壤类型、成土母质、海拔、土地利用、气候和坡度6个环境因子对全区土壤有机质含量变异的综合解释能力为47.9%。其中,土壤类型是最重要的影响因素,能独立解释其变异的36.0%,海拔和成土母质分别能独立解释28.5%和15.8%。气温对广西土壤有机质空间分布的影响比降水量更加显著,从而造成了广西土壤有机质整体呈南低北高的趋势。同时,土壤有机质对气温的敏感性在一定程度上受到降雨量的制约。此外,研究区农业耕作管理等因素对土壤有机质的影响也不容忽视。