The scale effects of the spatial autocorrelation measurement: aggregation level and spatial resolution

The scale effects of the spatial autocorrelation (SA) measurement has been explored for decades. However, the effects of the data aggregation levels and spatial resolution on the SA measurement are often confused. Whether the two types of scale effects are the same is still unclear and requires further investigation. We retrieved the land surface temperature (LST) from Landsat 8 images in 30 capital cities of China. By aggregating the LST images, we observed a decrease in the SA of the LST as the data aggregation level increased; this relationship can be fitted well with a negative logarithmic function. We derived an indicator to measure the scale effects intensity of SA, which was negatively correlated with the spatial complexity of LST. Both aggregating images and the increasing spatial resolution induce weaker SA, but the effect of the former was stronger. The aggregating images negatively affected the SA degree regardless of the spatial resolutions of the original images. The SA degrees of the aggregated images were far below those of the real-life images. This study suggests that the scale effects caused by aggregation levels and spatial resolutions are different, and cautions should be taken when applying relevant conclusions derived from aggregating images.     

Spatiotemporal scale dependency and other sensitivities in dynamic land-use change simulations

This study examines how land-use change simulation outcomes can vary based on the way the simulation model is applied, attempting to support informed model choices and model applications. This is accomplished through a series of experiments using a hypothetical model that represents the basic logic of various cell-based dynamic land-use change modeling environments. In the experiments, consideration is given to the sensitivity of the simulation results with respect to the following four application specifications: (1) the spatial resolution, (2) the temporal resolution, (3) the probability distribution, and (4) the degree of the influence of stochastic factors, under multiple growth scenarios. The experiments show that all four factors, particularly the spatiotemporal resolution and the degree to which stochastic factors are involved, can generate substantial variation in the simulation model outcomes. It is also found that the magnitude of the variation can be affected by changes in regional growth rates and the level of fluctuation, which determine the demand for new development to be allocated over the simulation time horizon.     

不同空间尺度的山洪灾害风险评价模型对比研究

以山洪灾害风险评价的多准则决策模型、最大熵模型、信息量模型三种常见模型为研究对象,选取河西走廊和张掖市为地理区划（大中）、市域（小）空间尺度研究区,构建山洪灾害风险评价指标体系,分别完成基于三种模型的两种空间尺度的山洪灾害风险评价制图,基于甘肃省地质灾害调查与区划报告数据从模型验证、空间自相关、精度对比和尺度效应等角度对比分析三个模型应用于不同空间尺度的适应性,并给出优选模型。结果表明：最大熵模型是河西走廊（地理区划）空间尺度上山洪灾害风险评价的优选模型;多准则决策模型不适用于张掖市（市域）空间尺度评价,且三个模型运行结果均没有河西走廊（地理区划）空间尺度上表现良好;三个模型的尺度效应明显,在地理区划空间尺度上应用较良好,缩小至市域空间尺度上模拟结果误差增大;不同空间尺度上,最大熵模型均优于多准则决策模型和信息量模型,适用于地理区划（大中）、市域（小）空间尺度的山洪灾害风险评价。     

Simulation of spatial population dynamics based on labor economics and multi-agent systems: a case study on a rapidly developing manufacturing metropolis

Spatial population dynamics affects resource allocation in urban planning. Simulation of population dynamics can provide useful information to urban planning for rapidly developing manufacturing metropolises. In such a metropolis with a concentration of immigrant labor forces, individual employment choices could have a significant effect on their residential decisions. There remains a need for an efficient method, which can simulate spatial population dynamics by considering the interactions between employment and residential choices. This article proposes an agent-based model for simulation of spatial population dynamics by addressing the influence of labor market on individual residential decisions. Labor economics theory is incorporated into a multi-agent system in this model. The long-term equilibrium process of labor market is established to define the interactions between labor supply and labor demand. An agent-based approach is adopted to simulate the economic behaviors and residential decisions of population individuals. The residential decisions of individuals would eventually have consequences on spatial population dynamics. The proposed model has been verified by the spatial dynamics simulation (2007 to 2010) of Dongguan, an emerging and renowned manufacturing metropolis in the Pearl River Delta, China. The results indicate that the simulated population size and spatial distribution of each town in Dongguan are close to those obtained from census data. The proposed model is also applied to predict spatial population dynamics based on two economic planning scenarios in Dongguan from 2010 to2015. The predicted results provide insights into the population dynamics of this fast-growing region.     

Neighborhood size and spatial scale in raster-based slope calculations

Raster-based slope estimation is routine in GIS. Like many other terrain attributes, the slope at a location is determined from elevations of surrounding cells. This spatial extent – ‘neighborhood size’ – is often treated as the ‘spatial scale’ of the calculation. In fact, neighborhood size and spatial scale are two connected yet different concepts, but few studies have investigated the relationship between them. The distinction is important because neighborhood size is under user control whereas spatial scale is merely implicit in the computational method. This article attempts to clarify and provide a more precise meaning of the two terms by considering slope operators from the standpoint of the frequency (or wavenumber) domain. This article derives analytical expressions for the amplitude response functions of four popular slope estimators. These are used to characterize the individual methods and also to show that the neighborhood size and spatial scale of a slope calculation are not numerically the same. In fact, because there is no single spatial scale that can be unambiguously associated with a given neighborhood size, neighborhood size cannot be an adequate indicator of spatial scale. Furthermore, this article shows that different indices of ‘scale’ yield different impressions about the action of a slope estimator and its response to changing neighborhood size. Therefore, it is necessary to examine the amplitude response function when investigating the spatial scale. The article also provides guidance for GIS practitioners when selecting a slope estimation method.     

县域空间贫困的地理识别研究——以宁夏泾源县为例

空间贫困研究是对多维贫困理论的丰富和发展,创新空间贫困度量方法是精准配置扶贫资源和提高农村扶贫质量的关键。本文借鉴国际空间贫困研究的基本理论思想,总结出了空间贫困三维结构分析框架,构建了县域空间贫困指标体系和地理识别方法,以宁夏泾源国家扶贫开发重点县为例,开展了空间贫困地理识别。研究发现：① 不同自然地理区SPI排序为侵蚀堆积河谷平川区（均值1.571）>剥蚀构造丘陵区（均值-0.199）>侵蚀构造石山区（均值-0.334）,表明侵蚀构造石山区贫困程度最大,是减贫重点区域,其中3个自然地理区空间贫困的共同特点是存在经济劣势（C₄~C₇、C₁₇）,主要差异是侵蚀堆积河谷平川区生态劣势（C₂₅、C₁₆）>经济劣势（C₅）>位置劣势（C₂₀）>政治劣势（C₈）;剥蚀构造丘陵区经济劣势（C₅~C₇、C₁₇）>位置劣势（C₁₈~C₂₀）>生态劣势（C₁₆、C₂₃~C₂₅）>政治劣势（C₈）;侵蚀构造石山区经济劣势（C₄~C₇、C₁₇）>政治劣势（C₈）>位置劣势（C₁₈~C₂₀）>生态劣势（C₁₆、C₂₁~C₂₅）。② 不同民族村SPI排序为汉族村（均值1.484）>回族村（均值1.262）>回汉混居村（均值-1.033）,表明县域尺度上回汉混居村是减贫重点村。不同民族村空间贫困的共同特征是由于距离最近市场远（C₁₇）形成的经济劣势,以及人均耕地面积（C₂₃）和农作物特别经济作物种植面积小（C₂₄）形成的生态劣势。不同民族村空间贫困的主要差异在于汉族村人居环境差和灾损率高形成的生态劣势,以及贷款不足（C₅）导致的经济劣势突出,回族村主要因文化知识素质较低（C₁₂、C₁₃）导致进入市场的机会成本高而形成经济劣势,回汉混居村因汉族和回族生产生活方式差异,造成扶贫资源配置困难,农户对扶贫政策满意率最低（C₈）,反映该类村空间贫困的政治劣势最为显著。③ 同一自然地理区域不同民族村空间贫困存在显著差异。侵蚀堆积河谷平川区汉族村、回族村和回汉混居村SPI均值分别为0.526、2.557和1.644,表明该类型区汉族村贫困程度高,经济劣势显著（C₅）;剥蚀构造丘陵区回族村和回汉混居村的SPI均值分别约为0.321和-1.934,表明该类型区回汉混居村贫困程度较高,经济劣势（C₂、C₆、C₇）和生态劣势（C₁₆）突出;侵蚀构造石山区汉族村、回族村和回汉混居村的SPI均值分别为1.031、-0.029和-0.842,表明该类型区回汉混居村贫困程度亦高,经济劣势（C₅~C₇、C₁₇）、政治劣势（C₈）、位置劣势（C₁₈~C₂₀）和生态劣势（C₁₆、C₂₅）并存。因此,侵蚀堆积河谷平川区的汉族村、剥蚀构造丘陵区和侵蚀构造石山区的回汉混居村是减贫重点村。     

An individual cognitive evacuation behaviour model for agent-based simulation: a case study of a large outdoor event

Route selection is a complex issue in simulating the pedestrian’s walking behaviour during the evacuation. In many current studies, the agent-based model has been widely used to simulate the individualized behaviour, and growing concern is given to the cognitive aspects in route selection. However, the influence of incomplete spatial knowledge and the preferences for different routing strategies has not been fully considered. To represent more reliable route choice by different pedestrians, this study presents an individual cognitive evacuation behaviour model which is mainly focusing on the individual differences in routing strategy and spatial knowledge. The model consists of two major components, namely the multiple routing strategies and personalized spatial cognitive road network. Based on a review of the previous studies, the potential factors that may affect the evacuation behaviour were discussed. The quantitative relationships between these potential factors and the pedestrian’s routing preference, as well as the possibilities of recognizing different roads, were explored using the regression method. As a case study, the proposed model was implemented using the data collected from a field survey of a real outdoor event. When applied in an agent-based simulation, the implemented model will support more reliable simulation of route choices corresponding to the pedestrians’ behaviour in the real event.     

顾及空间尺度效应的城市土地利用变化精细化模拟

城市土地利用变化模拟是优化土地资源配置的科学依据,提高其精细化程度和可靠性有助于准确把握城市用地发展趋势,对城市土地资源精准调控具有重要意义。基于宏观遥感分类的土地利用变化模拟,难以在街区尺度上揭示城市用地社会功能变化及精细化模拟中空间尺度效应来源和作用机理。本文联合遥感影像和POI数据识别出城市土地利用精细化特征,运用响应面法率定土地利用精细化模拟的最优空间尺度组合,在此基础上,利用CA-Markov模型开展了未来土地利用变化的精细化模拟。以武汉市中心城区为应用案例,研究结果表明：基于POI 的城市土地利用精细化识别方法,可以深度解析城市建设用地的社会功能,极大改善了传统基于遥感的土地覆被宏观解译效果;研究区土地利用变化元胞自动机精细化模拟的最优空间尺度组合是30 m元胞、7×7邻域以及冯诺依曼邻域类型,采用最优空间尺度组合能够提高土地利用变化精细化模拟的可靠性。响应面试验设计结果可有效识别精细化模拟过程中空间尺度效应的主要来源,并区分其对模拟精度的影响程度与正负效应;预计到2025年,研究区建设用地范围将继续向周边扩张,各类型用地之间互为交织,土地利用空间格局将呈更加破碎化趋势。     

空间尺度视角下的家庭能源消费研究进展与展望

能源是地理学研究的核心议题,家庭能源消费活动作为区域典型人地关系活动的形式,是能源地理精细化研究的一大趋势。论文基于地理学的空间尺度视角,回顾了能源地理研究的缘起、家庭能源消费的空间特征、形成机制及数据来源,对存在问题进行述评,并从当前家庭能源消费面临的机遇出发,对未来值得进一步深入研究的主题进行了展望和阐释。主要得到3个结论：① 构建全景式家庭能源数据调查框架,将地域类型与能源类型、数量、用途纳入调查的视域;② 中观尺度的研究有待加强,通过微观—中观—宏观尺度内关系的协调,从一个相互嵌套式的结构整体中去认识和把握家庭能源消费特征及规律;③ 构建家庭属性、地理因素、生活方式等多要素综合集成分析框架,剖析家庭能源消费空间特征的形成动因,实现对家庭能源消费空间分异过程的本质理解。研究有助于地理学能源领域的空间研究和实践的国际化,对中国家庭能源消费的理论视角补充具有积极的学科意义。     

Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change

Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.     

技术扩散和资本流动作用下中国区域空间结构演化——基于Agent的模拟

空间结构是区域的基本组成部分,在空间交互过程和区域经济发展方面起着重要的作用。本文的主要研究目标是在不同的道路交通系统作用下,中国区域空间结构的发展演化过程。这对于解决中国当前经济发展中的不平衡问题,探索使区域平衡发展的方法具有重要意义。与传统的研究方法不同,本文从自上而下建模的角度构建了一个基于Agent 的动态区域经济模型。在模型中,中国的每个地级市都被定义为企业Agent,拥有不同技术水平的劳动者Agent 为企业提供劳动力,并在Wilson 空间吸引力模型的作用下在区域之间迁移。技术作用于劳动力之上,随劳动者的迁移而扩散。资本由企业的投资行为而转移。模型中的道路交通线具有不同的权值,这意味着道路交通网是异质的。通过情景模拟,得到以下结论：在分级道路交通网的作用下,传统的中心-腹地扩散模式已不再适用。技术服从枢纽-网络结构从中心城市向外扩散。资本的流动也具有与技术扩散相似的过程和空间结构。交通条件可以在很大程度上影响区域的发展,同时自然条件仍然起着空间锁定的作用。优惠的税率政策可以在一定程度上打破这种空间锁定,有利于吸引劳动者迁移,提高当地的生产技术水平。     

Sensitivity of disease cluster detection to spatial scales: an analysis with the spatial scan statistic method

ABSTRACT

The spatial scan statistic method has been widely used for detecting disease clusters. Its results may be affected by scales, including the aggregation level of the input data and the population threshold used in the detection. Previous studies offered inconsistent findings, and few had considered both types of scales at the same time. Using 24 simulated datasets and two real disease datasets, we investigated the method’s sensitivity to the two types of scales. We aggregated the individual-level data into areal units of three levels, including county, town, and a 900 m grid. We detected clusters with three population thresholds, including 10%, 25%, and 50%. We used two measurements, distance between cluster centres and the Jaccard index, to quantify the consistency of clusters detected with different scale settings. We find: (1) the method is not greatly sensitive to the data aggregation level when the cluster is strong and in a place with high population density; (2) the method’s sensitivity to the population threshold is determined by the actual size of the true cluster; and (3) a regular grid with fine resolution is advantageous over the subjectively defined areal units. The process and findings may have broader meanings to similar spatial analyses.     

中国表层土壤全氮的空间模拟分析

基于第二次全国土壤普查5336个典型土壤剖面数据,分析表土全氮(A层)与环境因素的相关关系,利用多元回归模型和HASM模型结合的方法模拟中国国家尺度上表层土壤全氮的空间分布格局。结果表明:对350个检验点模拟结果的平均绝对误差和平均相对误差为0.67g·kg^-1和61.06%,与普通克里格法相比分别降低了0.05g·kg^-1和17.53%;对样点分布较少的西北地区的模拟结果也更符合实际情况。多元回归模型和HASM模型结合的方法考虑了环境因素的影响,可作为目前模拟大尺度土壤性质空间分布相对有效的方法。     

中国高新技术企业分布影响因素的空间异质性与尺度效应

影响高新技术企业分布的因素往往具有空间非平稳性,然而既有研究对其关注尚少。基于2017—2019年间认定的215791家高新技术企业数据,运用多尺度地理加权回归模型（MGWR）,刻画了中国高新技术企业的空间分布格局,识别了其影响因素的空间异质性与尺度效应,结果表明：① 2019年中国高新技术企业的空间分布呈现出在以“北上广深”为首位中心,以成渝与区域性中心城市为次位中心的高度集聚特征。② 企业内部因素、城市知识创造水平、技术创新水平、创新环境水平和外部连通水平共同影响了高新技术企业的空间分布。③ 影响高新技术企业分布的因素存在空间异质性,公司年龄、高校学生数量、互联网的影响呈现出“东-西”向空间分异格局,专利申请数、高新区、生活设施的正向影响呈现出“南-北”向空间分异格局,高铁的正向影响呈现出“东南-西北”向空间分异格局,研发费用投入对东北地区影响最大,市场化水平对京津冀和珠三角城市群地区影响最大。④ 影响因素存在尺度效应,靠近创新末端的变量具有更大的作用空间尺度。最后,本研究提出相关的政策建议,以期为高新技术产业的发展提供借鉴与参考。     

乡镇尺度上的山东省人口老龄化空间格局演变与影响因素研究

基于第五次和第六次人口普查数据,研究了乡镇尺度上山东省人口老龄化的空间格局及其演变特征,并采用空间计量方法定量探讨了其影响因素。研究发现：① 山东省人口老龄化发展迅猛,但乡镇街道之间在老龄化水平、老年人口密度和增长速度等方面均存在非常显著的地域差异。② 按照不同的分析维度,山东省乡镇街道涵盖了丰富的人口老龄化地域类型,其中的2个类型形成了典型的空间分异格局。一类主要聚集在县级以上城市及其周边地域,总体上具有老年人口低占比、高密度、高绝对增长、低相对增长的特点;另一类是主要分布在胶东地区、黄河三角洲地区、沂蒙地区和鲁西地区等4个区域的一般乡镇,大体表现为高占比、低密度、低绝对增长、高相对增长特征。③ 空间滞后模型回归结果显示,老龄化动态演变过程的不确定性更强、影响因素更为复杂,基期老龄化基础对其影响效应不显著,经济增长速度有显著负向影响,但经济发展水平对老龄化静态格局的影响却不显著,使得乡镇尺度上老龄化空间格局的演变机制呈现出特殊性。     

流域最佳管理措施空间配置优化研究进展

最佳管理措施(BMPs)是保护流域水环境免受农业生产活动导致的污染的一系列措施。在进行流域尺度BMPs空间配置时,一方面要考虑BMPs的生态环境效益,另一方面要考虑农业经济效益,因此流域管理决策者需要对这些措施进行空间配置优化。最佳管理措施空间配置优化(简称BMPs空间优化)是基于专家经验或者利用优化算法而得出的方案,并通过流域模型和经济模型评价其环境和经济效益,最后选择效益最优的方案,这也是当前农业非点源污染和水环境保护研究的前沿和热点。本文在介绍BMPs及其评价模型的基础上,对当前BMPs空间优化研究中的两种方法进行了剖析,对当前国内外BMPs空间优化研究现状进行了回顾和总结,最后,指出了BMPs空间优化研究中现存的问题,指出了今后BMPs空间优化研究的方向。     

中国大都市多中心空间演化过程的非均衡动态模拟

构建了一个基于垄断竞争、规模经济、空间成本、消费者多样化偏好以及商品和服务差异化的大都市多中心空间结构模型,并整合基于agent的建模,借助非均衡(out-of-equilibrium)动态模拟,探讨了人口规模和消费需求、区域间和区域内商业产品及服务的替代弹性、固定成本投入、区位和交通条件等因素的"持续"变化对的大都市空间结构演化的影响过程。模拟表明:以商业为代表的经济活动往往集聚在具有区位优势、规模经济和消费能力的大都市传统中心,而交通条件的持续改善、新兴郊区次中心与传统城市中心之间以及城区内部商业产品和服务的不断差异化对大都市的多中心空间结构有重要影响,但影响的程度不同。更重要的是,大都市的人口规模和消费能力对新兴郊区次中心的形成作用显著。     

社区尺度下流动人口聚居区空间识别与尺度效应——以北京市为例

以北京市为案例区,以土地利用与人口分布的同一性为基础,通过建立基于空间配置法的流动人口聚居区识别方法,实现了社区尺度流动人口聚居区的空间识别。在此基础上,对比街道尺度和社区尺度流动人口聚居区的识别结果,判断流动人口聚居区的空间尺度效应,探析流动人口聚居区空间尺度效应的形成原因,明确选择社区尺度作为识别空间单元的必要性。研究发现：基于空间配置法的社区尺度流动人口聚居区识别方法具有较高的准确度,能够有效识别案例区约90%的流动人口聚居区。流动人口聚居区具有显著的空间尺度效应,主要原因包括：较大空间尺度容易忽略区域内部社会空间差异、城乡分割的二元社会经济体制和高速城市化进程。研究结论将为流动人口聚居区系统化、精细化研究提供技术支撑和方法借鉴,为政府相关部门进行流动人口聚居区顶层制度设计提供科学依据。     

土地集约利用背景下城市人口规模效益与经济规模效益的评价

在理论评述基础上, 构建了城市规模经济内涵的概念框架, 进一步应用计量分析方法, 探讨城市人口规模效益与经济规模效益。研究发现:现阶段我国的城市空间规模扩张整体呈现以人口增长为主要推动力;100～200万人口规模的城市每单位人口占用的空间规模相对较小, 从吸纳人口的角度应优先发展;200万以上人口规模的城市每单位人口占用的空间规模相对较大, 但每单位经济规模的空间占用较少, 从发展经济、增强区域竞争力的角度应适度发展;土地的集约利用可通过发展不同规模和类型的城市, 进而构建合理的城镇体系这一区域规划途径缓解或解决。最后, 本文从土地集约利用的角度, 针对目前的城市与区域规划实践提出了建议。