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.     

融合地理探测器和地理加权回归的太原市建设用地变化因素研究

准确地识别城市化进程中建设用地的变化情况及其背后的驱动力,对城市后续的可持续发展具有重要意义。本文首先以2000—2020年遥感影像为基础,对太原市建设用地空间分布变化进行研究,然后结合地理探测器模型和地理加权回归模型,对研究区建设用地的空间分布影响驱动力因子进行研究,得到以下结论:除政策因素外,现有的城市建设用地空间分布变化还受到高程、交通、GDP、人口等因素的显著作用。太原市城市建设用地变化的布局不单是GDP变化、人口变化、海拔高度、公路网密度4个显著性因子均匀、独立、直接作用的结果,而是具有空间异质性的各因子两两交互作用后增效的产物。本文成果有望为城市建设用地驱动力研究提供一种新思路。     

长江经济带城市土地利用效率的影响因素及其空间溢出效应研究

在人多地少的基本国情下,提高城市土地利用效率是促进区域可持续发展和建设生态文明的内在要求。本文在对2009-2016年长江经济带108个地级及以上城市土地利用效率的研究基础上,采用空间相关性研究方法分析了时空分布格局,通过构建空间杜宾模型(SDM)对其空间溢出效应进行分析。研究结果表明:1)2009-2016年长江经济带城市土地利用效率整体呈现稳定上升趋势;基本呈现以上海、南京、武汉等不同城市群中心城市向外逐步降低的“中心-外围”的分布格局。2)通过构建空间杜宾模型分析发现,经济发展水平、产业结构有显著为正的直接效应和溢出效应;土地市场化水平有显著为正的溢出效应;城市固定资产投资对城市土地利用效率具有正的直接效应和负的溢出效应;政府财政支出水平则对城市土地利用效率的直接效应和溢出效应均不明显。3)不同影响因素对经济带上中下游城市土地利用效率的空间效应存在一定差异性。     

基于RS与GIS的宁波市鄞州区土地利用变化分析

利用1990年土地详查变更调查数据和2006年SPOT遥感影像数据,借助GIS空间分析能力,通过计算土地利用类型动态度、转移矩阵、扩展强度指数分析了鄞州区的城镇土地利用变化的时空特征。研究结果表明:1)鄞州区城市、建制镇、交通运输用地、工矿用地扩展的大部分来源于耕地;2)区各街道城镇建设用地扩展强度指数大于建制镇;3)建设用地扩展以新城区及宁波城区周边的街道镇为主。     

利用地理标签数据感知城市活力

高度活跃的城市是社会稳定发展的基础。基于地理标签感知的城市活力能够量化城市发展现状,探索城市活力的影响机制,为精细化城市治理提供技术支撑。传统城市活力研究依赖于街区的活力调查,时间长,费用高。本文研究利用兴趣点和社交媒体签到等地理标签数据,提出了城市活力度量指标,探索性分析城市活力的分布模式。基于土地利用、道路和建筑物等数据计算建成环境指标,构建城市活力和建成环境之间的普通线性回归与空间自回归模型,揭示了影响城市活力的建成环境因素。基于深圳市的试验结果表明:兴趣点和社交媒体签到数据能够较好地指示城市活力。深圳市的城市活力主要受商业用地、工业用地、土地混合利用以及路网密度、地铁站点密度的影响。住宅用地和建筑物占地密度对基于POI的城市活力具有显著影响。     

地理模拟优化系统(GeoSOS)及其在地理国情分析中的应用

第一次全国地理国情普查工作已基本完成,需要对所获取的地理国情监测数据进行空间统计分析,以得到地理国情的时空分布模式、演变规律及发展趋势,形成地理国情信息和知识。地理国情监测生成的基础数据源,可用于土地资源集约利用、城乡及城市群协调发展、生态环境保护等问题的空间智能决策分析,但目前有效的模拟分析工具还比较缺乏。笔者提出的地理模拟优化系统(GeoSOS)理论框架,耦合了地理过程模拟预测和空间优化,为上述研究提供了有利的理论支持和实用工具。为进一步方便与GIS进行无缝的链接,本文提出了基于ArcGIS插件的GeoSOS以方便进行高效的地理模拟分析。并以快速城市化地区的城市扩张与生态保护研究为例,通过在地理国情信息分析中的应用,得到了一些重要的发现。模拟结果显示,按照现在的发展趋势,广东省优化开发区域在2020年其土地开发强度将超出规定的约束指标,并发现各区域均存在大量城乡建设用地占用生态保护压力等级高和较高地块的现象,而基于土地开发强度和生态保护双重约束的城镇化扩张能够在满足约束指标的同时获得更紧凑的景观格局。表明GeoSOS及其ArcGIS插件可以成为地理国情信息分析的有利工具,并给出了今后需解决的问题和发展方向。     

Incorporating sprawl and adjacency measures in land‐use forecasting model: A case study of Collin County,TX

Sprawl measures have largely been neglected in land‐use forecasting models. The current approach for land‐use allocation using optimization mostly utilizes objective functions and constraints that are non‐spatial in nature. Application of spatial constraints could take care of the contiguity and compactness of land uses and can be utilized to address urban sprawl. Because a land‐use model is used as an input to transportation modeling, a better spatial allocation strategy for more compact land‐use projections will promote better transportation planning and sustainable development. This study formulates a scenario‐based approach to normative modeling of urban sprawl. In doing so, it seeks to improve the land‐use projections by employing a spatial optimization model with contiguity and compactness consideration. This study incorporates urban sprawl measures based on smart growth principles together with a mixed‐use factor, and adjacency consideration of nearby land uses. The objective function used in the study maximizes net suitability based on imposed constraints. These constraints are based on smart growth principles that enhance walkability in neighborhoods, promote better health for residents, and encourage mixed‐use development. The formulated model has been applied to Collin County, TX, a fast‐developing suburban county located to the north of the Dallas–Fort Worth metroplex. The suitability of land cells indicates the probability of conversion, which is calculated using spatial discrete choice analysis with Moran eigenvector spatial filtering for vacant cells at a resolution of 150 × 150 m employing factors of the built environment, and socioeconomic and demographic characteristics. This study demonstrates how spatial proximity between land uses, which has been ignored to date, can be used to control sprawl, resulting in better mixing of different land uses based on constraints imposed in a spatial optimization problem.     

A Bayesian characterization of urban land use configurations from VHR remote sensing images

The composition and arrangement of spatial entities, i.e., land cover objects, play a key role in distinguishing land use types from very high resolution (VHR) remote sensing images, in particular in urban environments. This paper presents a new method to characterize the spatial arrangement for urban land use extraction using VHR images. We derive an adjacency unit matrix to represent the spatial arrangement of land cover objects obtained from a VHR image, and use a graph convolutional network to quantify the spatial arrangement by extracting hidden features from adjacency unit matrices. The distribution of the spatial arrangement variables, i.e., hidden features, and the spatial composition variables, i.e., widely used land use indicators, are then estimated. We use a Bayesian method to integrate the variables of spatial arrangement and composition for urban land use extraction. Experiments were conducted using three VHR images acquired in two urban areas: a Pleiades image in Wuhan in 2013, a Superview image in Wuhan in 2019, and a GeoEye image in Oklahoma City in 2012. Our results show that the proposed method provides an effective means to characterize the spatial arrangement of land cover objects, and produces urban land use extractions with overall accuracies (i.e., 86% and 93%) higher than existing methods (i.e., 83% and 88%) that use spatial arrangement information based on building types on the Pleiades and GeoEye datasets. Moreover, it is unnecessary to further categorize the dominant land cover type into finer types for the characterization of spatial arrangement. We conclude that the proposed method has a high potential for the characterization of urban structure using different VHR images, and for the extraction of urban land use in different urban areas.     

城市用地空间信息分类与数字重建——以长春百年城市内部用地变化为例

针对单一应用遥感影像难以进行城市内部用地结构分类以及高精度城市内部用地多期空间数据叠置分析中位置误差问题建立了基于"分层分类"与"对象分割"的城市内部用地空间信息数字重建方法。实现对特大城市产业用地(住宅、商业、工业等)以及交通、水系、生态绿地等不同功能结构用地的高精度监测以及历史演变过程的重建。综合集成SPOT5,1︰1万地形图、历史地图及城市规划图等辅助信息对长春城市1905年以来城市用地信息进行分类。研究表明,在专家知识参与下人—机交互解译,集成多源空间信息对实现高精度城市用地空间信息重建具有较高的应用价值,该方法不仅能提高城市用地分类精度而且能提高城市用地空间信息提取效率以及多期空间数据叠置分析的定位精度。     

基于GIS和空间逻辑回归模型的城市空间扩展预测——以深圳市龙岗区为例

本文从土地利用的角度出发,分析了城市扩展的基本原理和过程,提出城市空间扩展直接原因在于城市发展对土地的需求,城市空间扩展预测可分为城市发展用地总体需求预测和空间扩展预测的观点,并采用优化组合预测模型对城市用地总体规模扩展进行建模,利用空间逻辑回归模型对空间分布扩展进行建模,两者共同构建了城市空间扩展预测模型,最后以深圳市龙岗区为例,进行了试验,验证了这种思路。     

基于元胞自动机复合模型的土地利用演化模拟——以北京市海淀区为例

为了在土地利用空间格局演化模拟的基础上,为未来城市土地利用规划及管理提供更为科学合理的决策依据,本文以北京市海淀区1996年、2002年及2008年3期土地利用数据为数据源,重点采用元胞自动机复合模型CA-Markov模型与多标准评价方法相结合的手段,构建元胞转移数量规则及空间位置转化规则,并分别构造3种不同大小的元胞邻域集合,进行土地利用格局的模拟及预测。试验中2008年土地利用模拟结果与实际土地利用数据Kappa系数高达0.856 1,表明CA-Markov模型结合多标准评价方法的模拟手段可行性较高,同时元胞邻域空间大小对模拟结果的精度有明显的影响。土地利用结构数据及模拟预测结果表明城市集约化现象明显,建设用地迅速扩张,占用大量耕地、园地用地,因此迫切需要促进城市土地利用的可持续发展。     

Simulating urban growth in a megalopolitan area using a patch‐based cellular automata

Although traditional cellular automata (CA)‐based models can effectively simulate urban land‐use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell‐based simulation strategies. This research proposes a new patch‐based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a pattern‐calibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000–2006–2012. The results from this model were compared with two common models: cell‐based CA and logistic‐patch CA. The proposed model yields a degree of patch‐level agreement that is 23.4 and 7.5% higher than those of these pre‐existing models, respectively. This reveals that the patch‐based CA model simulates actual development patterns much better than the two other models.     

新疆克拉玛依市商业地价空间分布规律研究

城市地价是一个具有时空性质的多维概念,在空间分布上具有较强的关联性和特殊性。但目前对城市地价空间分布规律方面的研究相对较少,还没有形成系统性的理论与方法依据,本文以克拉玛依市2007年土地调查商业用地数据和地价动态监测数据为依据,以ARCGIS软件为平台,采用地统计学的方法,对克拉玛依市商业用地价格的空间分布进行了研究。研究表明:地统计学的理论与方法对城市地价空间分布进行研究具有独特的优越性;城市商业用地地价的分布在空间上既有连续性,也存在变异性,受区域整体趋势的影响明显高于小范围的变异性,属于强空间自相关;商业用地地价分布整体呈现圈层结构,交通道路对地价的空间分布具有重要影响。     

An Evaluation of Four Different Wavelet Decomposition Procedures for Spatial Feature Discrimination in Urban Areas

Texture or spatial arrangement of neighborhood objects and features plays an important role in the human visual system for pattern recognition and image classification. The traditional spectral–based image processing techniques have proven inadequate for urban land use and land cover mapping from images acquired by the current generation of fine–resolution satellites. This is because of the high frequency spatial arrangements or complex nature of urban features. There is a need for an effective algorithm to digitally classify urban land use and land cover categories using high–resolution image data. Recent studies using wavelet transforms for texture analysis have generally reported better accuracy. Based on a high–resolution ATLAS image, this study illustrates four different wavelet decomposition procedures – the standard, horizontal, vertical, and diagonal decompositions – for urban land use and land cover feature extraction with the use of 33×33 pixel samples. The standard decomposition approach was found to be the most efficient approach in urban texture analysis and classification. For comparison purposes and to better evaluate the accuracy of wavelet approaches in image classification, spatial autocorrelation techniques (Moran's I and Geary's C ) and the spatial co–occurrence matrix method were also examined. The results suggest that the wavelet transform approach is superior to all other approaches.     

基于土地利用类型提高人口格网数据空间精度的方法

高空间精度的人口格网数据具有空间分辨率高、人口空间分布特征准确的特点,在受灾人群估计、城市规划建设等领域有广泛的应用。针对已有的公开人口格网数据集(如WorldPop世界人口格网数据集)存在人口空间分布特征在小尺度上刻画不准确、空间分辨率较低的问题,本文使用东营市土地利用类型数据,结合地类权重和面积权重对WorldPop数据进行空间精度优化,获得东营市东营区黄河路街道25 m人口格网数据。相较于WorldPop数据,经过本文方法处理后的数据集空间分辨率更优,在可视化对比中,能够更准确地刻画人口分布特征,并在各地类人口占比统计中人口空间分布与用地单元分布一致性更高。该方法为获取小区域高空间精度人口格网数据提供了一种新思路。     

漯河市近40年土地利用及景观格局变化特征研究

本文以河南省漯河市为例,以遥感解译的漯河市近40年来土地利用数据为基础,运用GIS叠置分析、土地利用转移矩阵等空间分析方法,结合景观格局指标,研究其改革开放以来土地利用及景观格局变化特征,为优化土地资源配置及科学的城市规划提供依据。结果表明:(1)1980年至2015年间漯河城镇用地面积呈显著增加趋势,扩张主要占用耕地;(2)林地和农村居民点用地总体呈增加趋势,2000年来其他建设用地面积迅速增加;(3)漯河市各景观类型总体呈平衡发展态势,耕地优势度有所下降,建设用地优势度不断上升;(4)人口增长与经济发展、交通条件改善及政府政策变动是影响漯河市土地利用变化和景观格局演变的主要因素。     

Evaluating the compatibility of multi-functional and intensive urban land uses

This research is aimed at developing a model for assessing land use compatibility in densely built-up urban areas. In this process, a new model was developed through the combination of a suite of existing methods and tools: geographical information system, Delphi methods and spatial decision support tools: namely multi-criteria evaluation analysis, analytical hierarchy process and ordered weighted average method. The developed model has the potential to calculate land use compatibility in both horizontal and vertical directions. Furthermore, the compatibility between the use of each floor in a building and its neighboring land uses can be evaluated. The method was tested in a built-up urban area located in Tehran, the capital city of Iran. The results show that the model is robust in clarifying different levels of physical compatibility between neighboring land uses. This paper describes the various steps and processes of developing the proposed land use compatibility evaluation model (CEM).     

基于GIS城镇扩展的空间格局分析

以城镇扩展为核心内容的城市土地利用/土地覆盖变化已经成为目前国内土地利用/土地覆盖变化(LUCC)研究的热点.基于某地2002年至2007年土地利用数据,提取城镇用地信息,利用全局和局部空间关联模型,分析了区域城镇扩展的空间分布特征,得出了相关研究成果.     

GIS在城区土地定级中的应用

城区土地定级研究是进行城区地价评估的基础.运用特尔菲法选取定级因素、因子并确定权重;借助GIS强大的空间数据和属性数据管理、分析功能,计算影响因素、因子的分值和定级单元的分值,并进一步对土地级别进行了划分;通过GIS的制图功能对定级结果以图形化的方式输出.     

利用时序手机通话数据识别城市用地功能

城市土地利用是人的活动与城市物质空间交互所表现出的综合结果,因此人的活动与城市土地利用功能密切相关;具有不同时间段人的活动的空间聚集与分散规律的区域,其所属的社会功能属性亦不相同。随着大数据时代的到来,以居民手机数据为代表的基于位置的服务数据（local basic service,LBS）大量出现,使得实现时空全覆盖和精细化地监测城市人的活动成为可能。因此,利用手机数据的优势,能够实现从人的角度来区分识别城市用地功能类型。利用手机通话详单数据（call detail records,CDRs）提取面向地块尺度的居民通话聚合时序特征,提出了一种城市土地利用类型谱聚类识别方法。以武汉市为例进行实验分析,结果表明,该方法识别城市土地利用的平均精度为54.6%,为探知城市土地利用空间分布提供了一个有效的方法。