德宏州国土空间开发格局驱动力研究

针对云南省德宏州国土空间开发格局形成机制复杂的问题,该文提出了一种基于冗余分析(RDA)的驱动力研究方法。使用景观格局指数对各乡镇建设用地空间格局进行定量分析,以自然和社会经济数据作为潜在的驱动因子,利用RDA方法对驱动因子进行约束性排序,分析得出人口和经济因素是主要驱动因素。人口众多、经济发达的乡镇,建设用地相对面积较大,并且建设用地更趋向于聚集。此外,自然因素、农业因素、政策因素等,也与人口和经济因素有联动作用,驱动国土空间形态与结构的形成。提出新的驱动力分析方法,一方面探讨冗余分析方法在国土空间开发格局驱动力分析中的应用潜力;另一方面为云南省第一次地理国情普查成果的深化应用提供典型应用示范。     

集成地理探测器与随机森林模型的城市人口分布格网模拟

精细尺度的城市人口分布信息是城市资源配置和综合管理的重要依据。本文以广州市越秀区、荔湾区、天河区、海珠区、白云区及黄埔区作为研究区域，基于人口统计、夜间灯光、兴趣点及土地利用等多源数据，利用地理探测器识别人口分布的影响因子，运用随机森林模型开展人口分布空间格网模拟研究。研究结果表明，与传统的相关分析相比，地理探测器能够更为准确地识别人口空间分布的重要影响因子。基于随机森林模型的人口分布格网模拟结果与街道（镇）实际人口的相关系数为0.774，平均相对误差约为30%。相比基于线性回归模型的模拟结果，随机森林模型的精度有明显提高。     

基于地理国情监测的城市土地利用变化分析

利用第一次全国地理国情普查数据,结合地理国情监测数据,采用土地利用变化转移矩阵、土地利用动态度模型以及空间分析等方法对某地土地利用总体变化和专项土地利用类型如农业用地、建设用地、基础设施用地情况进行监测分析,为城市政府部门在城镇化建设、城市管理、城市规划等提供科学的地理情报支持。     

地理国情在武汉市土地资源承载力评价中的应用

基于武汉市地理国情数据,围绕城市土地资源承载力问题,研究建立了一套城市土地资源承载力评价技术方法。首先,根据土地利用功能进行"三生用地空间"划分,将市、区有关经济统计指标按街道进行了尺度下推;其次,在分析评价适宜建设开发用地、已利用建设用地、可开发用地资源的基础上,开展了土地资源承载状态的综合评价;最后,利用地理加权回归分析方法,分析了人口、经济、交通因素对土地资源承载状态的影响。该方法将城市土地资源承载力评价由传统的人口承载力测算转换为开发建设状态评价,不仅为城市承载力评价提供了一种新的分析思路,也为制定精细化和差异化的城市建设用地资源配置和调控政策提供了科学依据。     

1986-2018年广州市建设用地扩张及其影响因素研究

针对快速城市化引起的一系列城市问题,利用扩张速率、等扇分析法、景观扩张指数等方法,分析了广州市19862018年建设用地的时空特征,通过多元回归和地理探测器探索建设用地时序演变和空间分异过程的影响因素及其交互作用,可为城市规划管理中优化城市发展模式和城市可持续发展提供技术支撑.研究结果表明:①1990-2000年是广州市建设用地迅速扩张期,此后有所回落并稳定发展;②建设用地扩张方向呈分阶段分区域进行的特征,主要向东南、西北和东北3个方向扩张;③建设用地扩张呈现蔓延型和填充型为主的扩张模式;④建设用地扩张时序演变过程的主要影响因素包括GDP、人口和地方财政收入;空间演变过程的主要影响因素包括与公路、铁路的距离、DEM,且因子间均为双因子增强关系.     

基于地理国情数据的基本公共服务空间格局分析

以地理国情普查及地理国情监测数据为基础,结合众多学者的研究,构建城市公共服务空间格局监测分析指标体系,确定监测分析方法,开展宜宾市基本公共服务空间格局监测分析,并对其与宜宾市人口、经济之间的相关性进行分析。分析得出,宜宾市基本公共服务密度分布呈片状分散分布,由城区-乡村逐渐降低。宜宾市基本公共服务密度与其人口密度、人均GDP成正相关关系,与人口密度相关性颇高,与人均GDP相关性不明显。     

基于基础地理数据统计的长三角城市主城区空间格局扩展研究

在快速城镇化时期,城市主城区的空间格局变化最为显著,以其复杂性和扩展性为突出特征。研究其扩展变化对认识城镇化、优化城市空间结构等具有重要意义。本文在基础地理数据统计的基础上,对长三角城市主城区2002~2012年的空间扩展进行了分析,探索影响其演变的若干因素。     

长江经济带人口城镇化空间格局及驱动力分析

针对驱动力对人口城镇化的空间变异特征分析不足的问题,该文以长江经济带市域为基本单元,借助社会经济数据,引入自然环境数据,建立了经济、社会、环境和土地因子的测度指标体系。运用探索性空间数据分析和地理加权回归模型,定量分析人口城镇化的空间格局、动力因子以及空间分异性。结果表明:人口城镇化水平区域间差异显著,呈现明显的空间集聚性,形成了"圈层集中-东西对立-中心联动-梯度明显"的分布格局。动力因子对各地区人口城镇化的影响呈现明显的空间异质性,环境因子和经济因子是长江经济带人口城镇化发展的主要影响因素,动力因子的影响程度和变化趋势能够客观的反映出区域经济发展、环境状况、社会发展和土地现状对城镇化发展在空间上的影响。     

基于地理国情监测的城市扩张分析研究

以地理空间数据、社会经济人文数据为数据源,借助RS、GIS技术对郑、汴一体化区域3个城市48年间8个监测年点建成区内的建成区范围、道路、绿化、建筑物用地、人口、GDP等监测要素进行获取和统计,并利用相关模型和指数,结合区域发展规划,对监测对象的发展趋势进行分析和研究,揭示其内在动力,为城市发展提供技术和数据支持.     

基于STIRPAT模型的长株潭城市建设用地扩展驱动因素分析

通过岭回归分析方法,尝试构建STIRPAT定量模型,进而分析社会经济因素对长株潭城市建设用地扩展的作用程度。首先,利用统计数据,从城市建设面积扩展动态度和扩展强度两个指标分析了1995—2014年长沙、株洲和湘潭3个城市建设用地扩张的阶段性特征;然后选定非农业人口、GDP、工业总产值、固定投资总额和第三产业增值作为建设用地扩张的驱动因子,应用岭回归分析方法拟合STIRPAT模型,构建出长株潭建设用地扩展驱动的量化模型。结果表明:工业总产值、固定资产投资总额和非农业人口分别是长沙、株洲和湘潭建设用地扩张的第一驱动因子;非农人口增长引发对城市建设用地的需求使得其在3个城市的驱动模型结果中均表现出较强的影响力。     

Modeling Urban Growth Using GIS and Remote Sensing

Based on remote sensing and GIS, this study models the spatial variations of urban growth patterns with a logistic geographically weighted regression (GWR) technique. Through a case study of Springfield, Missouri, the research employs both global and local logistic regression to model the probability of urban land expansion against a set of spatial and socioeconomic variables. The logistic GWR model significantly improves the global logistic regression model in three ways: (1) the local model has higher PCP (percentage correctly predicted) than the global model; (2) the local model has a smaller residual than the global model; and (3) residuals of the local model have less spatial dependence. More importantly, the local estimates of parameters enable us to investigate spatial variations in the influences of driving factors on urban growth. Based on parameter estimates of logistic GWR and using the inverse distance weighted (IDW) interpolation method, we generate a set of parameter surfaces to reveal the spatial variations of urban land expansion. The geographically weighted local analysis correctly reveals that urban growth in Springfield, Missouri is more a result of infrastructure construction, and an urban sprawl trend is observed from 1992 to 2005.     

Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules

The dynamic relationships between land use change and its driving forces vary spatially and can be identified by geographically weighted regression (GWR). We present a novel cellular automata (GWR-CA) model that incorporates GWR-derived spatially varying relationships to simulate land use change. Our GWR-CA model is characterized by spatially nonstationary transition rules that fully address local interactions in land use change. More importantly, each driving factor in our GWR model contains effects that both promote and resist land use change. We applied GWR-CA to simulate rapid land use change in Suzhou City on the Yangtze River Delta from 2000 to 2015. The GWR coefficients were visualized to highlight their spatial patterns and local variation, which are closely associated with their effects on land use change. The transition rules indicate low land conversion potential in the city’s center and outer suburbs, but higher land conversion potential in the inner near suburbs along the belt expressway. Residual statistics show that GWR fits the input data better than logistic regression (LR). Compared with an LR-based CA model, GWR-CA improves overall accuracy by 4.1% and captures 5.5% more urban growth, suggesting that GWR-CA may be superior in modeling land use change. Our results demonstrate that the GWR-CA model is effective in capturing spatially varying land transition rules to produce more realistic results, and is suitable for simulating land use change and urban expansion in rapidly urbanizing regions.     

Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data

In this study, we explored the spatial and temporal patterns of land cover and land use (LCLU) and population change dynamics in the St. Louis Metropolitan Statistical Area. The goal of this paper was to quantify the drivers of LCLU using long-term Landsat data from 1972 to 2010. First, we produced LCLU maps by using Landsat images from 1972, 1982, 1990, 2000, and 2010. Next, tract level population data of 1970, 1980, 1990, 2000, and 2010 were converted to 1-km square grid cells. Then, the LCLU maps were integrated with basic grid cell data to represent the proportion of each land cover category within a grid cell area. Finally, the proportional land cover maps and population census data were combined to investigate the relationship between land cover and population change based on grid cells using Pearson's correlation coefficient, ordinary least square (OLS), and local level geographically weighted regression (GWR). Land cover changes in terms of the percentage of area affected and rates of change were compared with population census data with a focus on the analysis of the spatial-temporal dynamics of urban growth patterns. The correlation coefficients of land cover categories and population changes were calculated for two decadal intervals between 1970 and 2010. Our results showed a causal relationship between LCLU changes and population dynamics over the last 40 years. Urban sprawl was positively correlated with population change. However, the relationship was not linear over space and time. Spatial heterogeneity and variations in the relationship demonstrate that urban sprawl was positively correlated with population changes in suburban area and negatively correlated in urban core and inner suburban area of the St. Louis Metropolitan Statistical Area. These results suggest that the imagery reflects processes of urban growth, inner-city decline, population migration, and social spatial inequality. The implications provide guidance for sustainable urban planning and development. We also demonstrate that grid cells allow robust synthesis of remote sensing and socioeconomic data to advance our knowledge of urban growth dynamics from both spatial and temporal scales and its association with population change.     

武汉市地面沉降时空分异特征及地理探测机制

城市地区地面沉降造成地面高程损失,威胁各类设施的安全运行,影响地表径流和水文循环,监测地面沉降现状并揭示其形成机制,对于城市可持续发展具有重要意义。以2007—2011年的ALOS-PALSAR影像和2015—2019年的Radarsat-2影像为数据源,基于小基线集技术（small baseline subset interferometric synthetic aperture radar, SBAS-InSAR）获取武汉市两个监测阶段的地面沉降速率、沉降时间序列,并利用地理探测器揭示规划单元尺度地面沉降的主导驱动因子及驱动因子之间的交互作用机制。结果表明:(1)2007—2011?年和2015—2019年地面沉降平均速率分别为-3.53 mm/a和-1.48 mm/a。地面沉降较为显著的区域:2007—2011年,是汉口、沙湖沿岸及以北、南湖以西和白沙洲地区;2015—2019年,是汉口、沙湖北和白沙洲地区。(2)?局部性、阶段性、与自然条件及人类活动相关是两个时期武汉市地面沉降演变的3个特点。(3)水文地质条件作为必要条件,通过与地面荷载、地下空间开发、工程施工因素交互作用形成武汉市地面沉降时空格局,2007?—2011年的工程施工因素、2015—2019年的地面荷载因素与水文地质条件交互作用明显较强。     

New urban settlements in a perspective of public and private interests. Case study: a Swedish municipality within the hinterland of the Stockholm city

The changes of land use patterns and urban structures could be seen as the dynamic result of the trade off between public and private interests. Thereby the land use change is to some extent unpredictable. The focus in the current study is to measure the importance of spatial location factors regarding new residential and commercial buildings in relation to existing urban amenities and political guidelines. The relative importance of the location factors was studied by multinomial regression analysis. Results from this study reveal that the location profiles of new urban object types attained here indicate strong correspondence with local political land use guidelines and to clustering. The spatial distribution of new urban settlements does not in general correspond to the monocentric urban scheme where firms and residents locate in spatial proximity to urban centres.      

武汉市体育场馆空间分布分析与研究

以武汉市为研究区域,运用密度分析和空间自相关探索武汉市体育场馆空间分布的聚集性;利用空间二元相关性定量地分析体育场馆空间分布与经济要素、社会要素以及道路网的相关性。结果表明,武汉市体育场馆在中心城区呈聚集并向外围扩散;人口密度、生产总值以及道路网密度都在不同程度上影响着体育场馆的空间布局。武汉市体育场馆的空间分布受经济和交通情况的影响比较大。通过多元回归模型,预测随着武汉城市中心及以外地区经济的发展、道路的完善以及政府的大力支持,乡村郊区的体育场馆数量将不断增多,武汉市体育馆将向着中心城区密集一乡村郊区普及的分布模式转变。     

Multiple intra-urban land use simulations and driving factors analysis: a case study in Huicheng,China

Simulations of intra-urban land use changes have gradually attracted more attention as these approaches are extremely helpful in regard to decision making and policy formulation. While prior studies mostly focused on methods of developing intra-urban level simulations, very little research has been conducted explain the factors driving intra-urban land use change. Urban planners are highly concerned with how inner-city structures are formed and how they function. Here, to simulate multiple intra-urban land use changes and to identify the contribution of different driving factors, we developed a random forests (RF) algorithm-based cellular automata (CA) simulation model. In this study, the model applied diverse categories of spatial variables, including traffic location factors, environmental factors, public services, and population density, as the driving factors to enhance our understanding of the dynamics of internal urban land use. The CA model was tested using data from the Huicheng district of Huizhou city in the Guangdong province of China. The Model was validated using actual historical land use data from 2000 to 2010. By applying the validated model, multiple intra-urban land use maps were simulated for 2015. Simultaneously, spatial variable importance measures (VIMs) were calculated by using the out-of-bag (OOB) error estimation approach of the RF algorithm. Based on the calculation results, we assessed and analysed the significance of each intra-urban land use driver for this region. This study provides urban planners and relevant scholars with detailed and targeted information that can aid in the formulation of specific planning strategies for different intra-urban land uses and support the future evolution of this area.     

基于GIS的丘陵区耕地景观格局时空演变特征分析

以四川省绵阳市涪城区为例,运用景观格局原理与GIS空间分析方法,分析1996--2009年期间耕地景观时空格局及其演变机理（驱动力）。结果表明：13年间,研究区耕地面积呈急剧减少的趋势,变化率明显高于省内丘陵区平均水平;耕地景观空间格局稳定性逐步降低,格局时空变化的地形分异特征显著;变化趋势受坡度、新增建设用地、人口密度、高程、城镇化水平、GDP、起伏度等负向驱动力与灌溉条件、等级公路水平、土地整理程度等正向驱动力因子共同影响。     

Measures of spatio-temporal accuracy for time series land cover data

Remote sensing is a useful tool for monitoring changes in land cover over time. The accuracy of such time-series analyses has hitherto only been assessed using confusion matrices. The matrix allows global measures of user, producer and overall accuracies to be generated, but lacks consideration of any spatial aspects of accuracy. It is well known that land cover errors are typically spatially auto-correlated and can have a distinct spatial distribution. As yet little work has considered the temporal dimension and investigated the persistence or errors in both geographic and temporal dimensions. Spatio-temporal errors can have a profound impact on both change detection and on environmental monitoring and modelling activities using land cover data. This study investigated methods for describing the spatio-temporal characteristics of classification accuracy. Annual thematic maps were created using a random forest classification of MODIS data over the Jakarta metropolitan areas for the period of 2001–2013. A logistic geographically weighted model was used to estimate annual spatial measures of user, producer and overall accuracies. A principal component analysis was then used to extract summaries of the multi-temporal accuracy. The results showed how the spatial distribution of user and producer accuracy varied over space and time, and overall spatial variance was confirmed by the principal component analysis. The results indicated that areas of homogeneous land cover were mapped with relatively high accuracy and low variability, and areas of mixed land cover with the opposite characteristics. A multi-temporal spatial approach to accuracy is shown to provide more informative measures of accuracy, allowing map producers and users to evaluate time series thematic maps more comprehensively than a standard confusion matrix approach. The need to identify suitable properties for a temporal kernel are discussed.