Evaluating representation and scale error in the maximal covering location problem using GIS and intelligent areal interpolation

A common problem in location-allocation modeling is the error associated with the representation and scale of demand. Numerous researchers have investigated aggregation errors associated with using different scaled data, and more recently, error associated with the geographic representation of model objects has also been studied. For covering problems, the validity of using polygon centroid representations of demand has been questioned by researchers, but the alternative has been to assume that demand is uniformly distributed within areal units. The spatial heterogeneity of demand within areal units thus has been modeled using one of two extremes – demand is completely concentrated at one location or demand is uniformly distributed. This article proposes using intelligent areal interpolation and geographic information systems to model the spatial heterogeneity of demand within spatial units when solving the maximal covering location problem. The results are compared against representations that assume demand is either concentrated at centroids or uniformly distributed. Using measures of scale and representation error, preliminary results from the test study indicate that for smaller scale data, representation has a substantial impact on model error whereas at larger scales, model error is not that different for the alternative representations of the distribution of demand within areal units.     

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.     

长江经济带城市群要素集聚能力差异的比较研究

城市群作为当前区域发展的主要空间载体,在区域发展竞争中扮演着越来越重要的角色。论文结合互联网大数据抓取等手段,以长江经济带5个城市群为研究对象,对城市群间要素集聚能力差异进行比较研究,发现：① 长江经济带城市群间要素集聚能力差异与长江经济带社会经济发展的区域差异相吻合,城市群要素集聚格局呈现出由东向西梯度衰减的分布特征;② 城际层面要素集聚能力差异的两极分化现象显著,以城市群首位城市为中心的要素集聚“核心—边缘”空间结构特征较为鲜明;③ 长江经济带城市群内科技创新、对外开放、金融等要素的集聚格局具有明显的差异分化和集聚分布指向特征;④ 长江经济带城市(群)要素集聚能力等级规模结构分布总体符合位序-规模法则,可分为“均衡型”和“离散型”2种类型,首位城市要素集聚首位度随城市群要素集聚能力高低呈现出“两端高、中间低”的特征。     

区域发展的空间失衡模式与状态评估——以江苏省为例

从收入增长和生态改善的空间均衡角度来看,区域发展空间失衡是空间开发状态与其区位供给能力不相匹配,空间的经济社会活动没有按照开发供给的比较优势进行地区分工配置。当区域开发规模及程度超过其可能的供给容量视为开发过度;若没有充分利用供给容量,则为开发不足。以江苏省县(县级市) 单元为例,采用开发强度与反映空间供给能力的生态重要性指数、环境敏感性指数、规模集聚指数、资源保障指数、运输经济指数的相关关系以及协调度,可以判断和评价空间失衡的状态。同时,资源环境价格市场不完善,政府调控要素导向不合理以及开发导向的财税和政绩考核体系等安排,都是导致空间失衡的制度原因。     

A multicriteria spatial decision support system for solving emergency service station location problems

Earthquakes occurring in urban areas constitute an important concern for emergency management and rescue services. Emergency service location problems may be formulated in discrete space or by restricting the potential location(s) to a specified finite set of points in continuous space. We propose a Multicriteria Spatial Decision Support System to identify shelters and emergency service locations in urban evacuation planning. The proposed system has emerged as an integration of the geographical information systems (GIS) and the multicriteria Decision-Making method of Preference Ranking Organization Method for Enrichment Evaluation IV (PROMETHEE IV). This system incorporates multiple and often conflicting criteria and decision-makers’ preferences into a spatial decision model. We consider three standard structural attributes (i.e., durability density, population density, and oldness density) in the form of spatial maps to determine the zones most vulnerable to an earthquake. The information on these spatial maps is then entered into the ArcGIS software to define the relevant scores for each point with regards to the aforementioned attributes. These scores will be used to compute the preference functions in PROMETHEE IV, whose net flow outranking for each alternative will be inputted in ArcGIS to determine the zones that are most vulnerable to an earthquake. The final scores obtained are integrated into a mathematical programming model designed to find the most suitable locations for the construction of emergency service stations. We demonstrate the applicability of the proposed method and the efficacy of the procedures and algorithms in an earthquake emergency service station planning case study in the city of Tehran.     

基于ESDA的中国年画网络店铺区位演化

通过收集淘宝网络店铺中的中国年画店铺基本信息,基于地级市空间统计单元,运用探索性空间数据分析理论中的全局自相关、局部自相关和热区分析等方法,对中国年画网络店铺区位的演化特征进行定量分析与挖掘。研究结果表明：1）中国年画网络店铺的区位演化整体上具有显著的空间自相关性,空间聚集特征明显;2）店铺区位局部差异明显,京、津、冀、鲁等省市构成了店铺区位选择的区域中心,并对周边城市产生辐射;3）店铺区位选择的热区从呈斑块状、阶梯状地域分布,逐渐演化为京、津、冀、鲁等稳定连片热区;4）总结了影响年画网络店铺区位选择的3个主要因素：区域经济因素、传统文化因素和消费者个性化需求因素。     

Identification of field attribute error under different models of spatial variation

Abstract

Recent developments in theory and computer software mean that it is now relatively straightforward to evaluate how attribute errors are propagated through quantitative spatial models in GIS. A major problem, however, is to estimate the errors associated with the inputs to these spatial models. A first approach is to use the root mean square error, but in many cases it is better to estimate the errors from the degree of spatial variation and the method used for mapping. It is essential to decide at an early stage whether one should use a discrete model of spatial variation (DMSV—homogeneous areas, abrupt boundaries), a continuous model (CMSV—a continuously varying regionalized variable field) or a mixture of both (MMSV—mixed model of spatial variation). Maps of predictions and prediction error standard deviations are different in all three cases, and it is crucial for error estimation which model of spatial variation is used. The choice of model has been insufficiently studied in depth, but can be based on prior information about the kinds of spatial processes and patterns that are present, or on validation results. When undetermined it is sensible to adopt the MMSV in order to bypass the rigidity of the DMSV and CMSV. These issues are explored and illustrated using data on the mean highest groundwater level in a polder area in the Netherlands.     

Where is my neighborhood? A dynamic individual-based definition of home ranges and implementation of multiple evaluation criteria

With the growing interest in studying characteristics of geographical context and its influence upon people, the concept of home range has been a focus of scholarly research. Home ranges are studied extensively across multiple disciplines, with literature supporting different operationalization techniques. This article argues that many of the existing approaches are not dynamic and versatile enough and do not provide reliable solutions for estimating individual home ranges. We additionally argue that many of current studies lack robust evaluation approaches. Recent evidences suggest that the usual approaches, which often exclusively rely on a single validation criterion, are not reliable and may be influenced by inferential errors. This study aims to tackle the exiting limitations in definition and operationalization of individual-based home range models and provide a more robust solution for their evaluation and comparison. Using data collected through public participation GIS we develop an applied, dynamic, and parametric model of individual home ranges. Subsequently, we propose multiple criteria comprising five validation hypotheses to evaluate model's effectiveness. We argue that application of this approach in evaluating spatial delimitation models can ameliorate the risk of biased validation resulting from inferential errors. The evaluation results indicate a substantial improvement in coverage of visited points compared to previously used static methods. Consequently, this paper draws a number of conclusions that can serve as guidelines for future research. This paper highlights the strengths and weaknesses of the proposed method and explains how it can be improved and employed in future studies investigating contextual effects on residents.     

基于Voronoi模型的海南岛旅游资源集合体空间边界提取

旅游资源是旅游业发展的物质条件,是开展各项旅游活动的载体和基础.旅游资源分类方法和评价理论研究已取得了较大的进展,但在旅游资源调查与规划实践中,以有研究通常将一个景区或大规模地理实体与小规模实体在同一个标准下衡量与对比,未考虑旅游资源的地理空间尺度特征.不同尺度的旅游地域空间,旅游资源评价、规划方法及其开发方向都不同....     

环莫干山民宿的时空分布特征与成因

民宿是中国乡村振兴和旅游转型升级过程中涌现出的新兴业态和空间利用方式,但学术关注滞后。环莫干山地区是目前中国最典型的民宿发展集聚区之一。本研究通过官方统计资料和实地调研获取环莫干山民宿空间发展数据,构建了ArcGIS和地理探测器结合的民宿空间过程分析方法,基于目的地的微观尺度系统地探讨环莫干山民宿时空分布特征和成因。研究发现：① 2011—2017年环莫干山民宿呈现出逐年增长的态势,空间分布范围不断扩大,空间发展方向出现较大变化,逐渐趋向镇域均衡性发展。目前已经在莫干山风景区毗邻村域形成以“后坞-仙潭-燎原-劳岭-兰树坑”为中心的环莫干山面状核心集聚区和边缘村域多点集聚的民宿空间发展结构。不同档次民宿的时空发展趋势具有一定差异。② 环莫干山民宿的空间过程由集聚到强烈集聚,不同档次民宿的集聚程度均呈现出快速提升的趋势。空间发展过程中早期民宿集聚点扩散效应显著。空间集聚过程与地形地势、核心风景区、交通干线和居民点密切相关。③ 风景景观、发展基础、社会因素和区位因素是环莫干山民宿景观格局异质性的重要决定因素维度,但不同等级民宿的空间决定因素维度和指标差异性显著,两因素维度交互作用对于民宿的空间影响力更为显著。研究对于丰富接待业空间选址的类型研究,总结中国典型民宿集聚区空间发展模式具有重要理论意义,同时对于民宿产业选址和民宿集聚区规划实践具有一定现实意义。     

中国产业空间格局演化与空间转型发展态势

基于城市层面三次产业的产值数据,应用空间统计标准差椭圆方法全面分析了 2003-2016年中国产业空间格局演化特征与转型发展态势.研究发现,2003-2016年中国三次产业空间中心均向内陆方向移动,且第二产业空间移动距离最大;第一产业空间离散化发展,空间聚集度减小;第二产业正在加快由沿海向内陆地区空间集中化聚集发展,空...     

基于POI数据的广州零售商业中心热点识别与业态集聚特征分析

商业中心是城市零售活动的重要载体,优化商业资源在城市内部空间的合理配置,摸清不同零售经营形态的区位选择,显得尤为重要。以面向公众服务的商业机构兴趣点（POI）数据为研究对象,提出一种城市商业中心与零售业态集聚区识别的方法;以广州市为例,分析商业活动的热点地区以及零售业态集聚区的空间分布特征。研究表明：① 根据核密度估计法提取的商业中心在等级上表现出由城市中心圈层向外围圈层扩散的趋势,结果符合客观事实。② 以街区为单元,商业网点密度符合局域Getis-Ord G^*指数统计特征的热点区域主要分布在越秀区和天河区,广州市零售业发展的双核心空间格局已经形成。③ 不同的零售业态对商业集聚的区位选择具有显著差异性,百货商店、超市、便利店等零售经营形态的空间集聚特征与该业态的市场定位、经营模式及选址策略基本吻合。总体来看,基于POI数据的广州零售业集聚空间分析结果能够反映实体零售企业行为与广州商业经济分布的相关性,有助于提高政府部门商业规划和零售商选址前期研究的客观性和科学性。     

面向离散点的空间权重矩阵生成算法与实证研究

采用阈值法和k-近邻法度量空间上离散点间的空间邻接关系,针对不同的距离计算方式(欧式距离和曼哈顿距离)设计了面向离散点的空间权重矩阵生成算法,使用C#语言在计算机上实现。用该算法对收集的8 367个常州市地价样点构建了不同土地用途地价样点的空间权重矩阵,并计算出分用途的常州市城市地价空间自相关指数。     

航空流网络解构与空域资源动态配置研究——以京-成空中廊道为例

空中廊道作为一种新型航空流组织与空域资源分配方式,是一种复杂的航空流网络,解析其结构特征并评估其结构性能是实现空域资源动态配置的前提。该文通过构建网格时空图方法,以京—成空中廊道为例进行航空流网络解构研究,包括基于航迹簇的网络构型、流量时空波动与航空流聚合分化三方面,据此提出空域资源动态配置方案。研究结论如下:1)以多种邻近阈值指示航迹交叉角度持续增大,检测出航空流网络存在平行流、交叉流、全覆盖流和半覆盖流4种构型,基于其占比和特征的显著差异性提出并行扩容调整方案;2)以高频数网格点状或短线状集中分布指示流量时空波动,检测出航空流网络存在两个不同内涵的时间高峰、以距离中值区为近似对称轴的空间高峰以及在区段间漂移的时空交互高峰,进而提出时间错峰调整方案;3)以网格属性指示聚合分化点的分布,检测出航空流网络阶梯递减、高值集聚和叠合特征,相应提出动态快速分化分流调整方案。     

新时期国土空间治理单元功能认知及其融合路径——以成都市为例

国土空间治理是新时期中国国土资源开发、利用、保护、整治及修复的重要战略手段。当前国土空间治理体系在空间尺度上存在主导功能衔接失配,亟待统一不同尺度空间单元以统筹治理边界、提高治理效率。据此,面向传统治理单元管制边界的针对性问题导向,提出“通过不同理念或方式界定的国土空间单元具有不同的功能导向”等研究假设,并构建土地利用冲突、生态系统障碍、社会发展状态等指数在行政区划单元、自然生态单元、社会发展单元等治理单元上以成都市为例进行验证。结果表明,研究区国土空间治理问题属性的空间格局特征差异化显著,人类活动范围与自然环境限制之间的矛盾是导致区域内治理问题发生的核心因素。在空间相关性分析方面,所有治理问题类型在不同空间单元上均呈现显著自相关性,且均通过0.05显著性检验,但不同空间单元具有理论指向性特点。其中,社会发展单元更适合解决经济发展动力差距问题,自然生态单元更适合解决生态系统连通阻力问题,行政区划单元则更适合解决事权管理冲突问题。在此基础上,利用景观单元协调不同尺度空间单元的问题属性,将研究区划分为高山地带发展动力衰弱型（22.98%）、丘陵地带治理属性均衡型（11.55%）、乡村地区管理冲突加剧型（44.57%）、城乡边缘区发展主导复合型（9.44%）、核心建成区连通阻力增强型（11.46%）5类空间,进而实现多尺度空间单元的跨行政区融合路径。综合而言,有必要建立管理-保护-发展功能嵌套的空间单元集成体系,通过细化景观尺度将权力和责任从政府机构拓展至更广泛的行动参与者,系统地提升国土空间治理效能。     

生产性服务业的空间研究进展及其评述

20世纪80年代以来,人文地理学开始从空间角度探讨生产性服务业的一些问题,在这个过程中逐步形成了相关的理论和研究议题.其中从空间角度研究生产性服务业的理论主要分析其区位选择与空间分布,形成了中心地理论、竟租理论、空间选择性扩散理论和集聚理论等理论,研究议题则涉及生产性服务业的区位选择与模式、空间分布、生产性服务企业的空...     

山地城市格局对餐饮业区位选择影响的空间异质性

城市格局对产业布局有深刻影响,山地城市尤甚。餐饮业是城市商业的重要“引流业态”,其布局与城市居住、交通、公共服务等空间功能密切相关,山地城市格局对餐饮业布局影响的空间异质性尤为明显和特殊。以典型山地城市重庆主城区为研究对象,基于多源兴趣点（POI）数据,运用核密度、空间自相关、地理加权回归等空间分析工具,探究山地城市格局对餐饮业区位选择影响的空间异质性。结果表明：① 重庆主城区餐饮业空间布局形态呈凝聚多中心特征,形成“一主两副四次级”的多中心空间结构,是重庆主城区“多中心,组团式”城市格局的缩影。② 餐饮业发育成熟度与山地城市扩展时序密切相关,且空间分布方向与城市扩展方向一致。③ 同一类城市空间因子对不同城市组团的餐饮业分布具有不同甚至相反的影响力,相较圈层式单中心城市空间因子作用模式的空间异质性更为显著,其本质原因在于山水自然分割和城市扩展的特殊性导致的中心组团和外围组团资源要素配置不均。④ 不同城市空间因子对餐饮业布局的作用力也具有空间异质性,城市居住、商业、交通、公共服务、休闲空间等5类因子在不同区域对餐饮业区位选择均有不同程度正向促进作用,其中城市商业空间因子对餐饮业区位选择影响最大。     

Forthcoming events

Spatial data uncertainty models (SDUM) are necessary tools that quantify the reliability of results from geographical information system (GIS) applications. One technique used by SDUM is Monte Carlo simulation, a technique that quantifies spatial data and application uncertainty by determining the possible range of application results. A complete Monte Carlo SDUM for generalized continuous surfaces typically has three components: an error magnitude model, a spatial statistical model defining error shapes, and a heuristic that creates multiple realizations of error fields added to the generalized elevation map. This paper introduces a spatial statistical model that represents multiple statistics simultaneously and weighted against each other. This paper's case study builds a SDUM for a digital elevation model (DEM). The case study accounts for relevant shape patterns in elevation errors by reintroducing specific topological shapes, such as ridges and valleys, in appropriate localized positions. The spatial statistical model also minimizes topological artefacts, such as cells without outward drainage and inappropriate gradient distributions, which are frequent problems with random field-based SDUM. Multiple weighted spatial statistics enable two conflicting SDUM philosophies to co-exist. The two philosophies are ‘errors are only measured from higher quality data’ and ‘SDUM need to model reality’. This article uses an automatic parameter fitting random field model to initialize Monte Carlo input realizations followed by an inter-map cell-swapping heuristic to adjust the realizations to fit multiple spatial statistics. The inter-map cell-swapping heuristic allows spatial data uncertainty modelers to choose the appropriate probability model and weighted multiple spatial statistics which best represent errors caused by map generalization. This article also presents a lag-based measure to better represent gradient within a SDUM. This article covers the inter-map cell-swapping heuristic as well as both probability and spatial statistical models in detail.     

COVARIANCE METHODS FOR SPATIO-TEMPORAL ANALYSIS

Geographers have adopted a variety of approaches to the analysis of variations in phenomena over space and through time. Investigations of spatio-temporal problems using linear models commonly assume that relationships among variables are stable, either among the spatial units employed or over the time points with which the analysis is concerned. Covariance procedures provide a convenient means for examining the spatial and temporal variability of relationships embodied in such models. In this article an empirical example of employment in agriculture demonstrates the application of the methods.