Regularities in Spatial Information Processing: Implications for Modeling Destination Choice

This paper investigates the determinants of spatial knowledge and how our knowledge of space varies according to geographic location. By using data on U.S. city names recalled at 22 test locations, a multivariate model of the information surface specific to each test location is calibrated. This model links the probability of a city being recalled from memory to its distance from the test site, its population size, its location with respect to other cities, and whether or not it is a state capital. The paper then suggests that these recall data provide information on spatial knowledge surfaces from which large-scale spatial choices, such as migration destinations, are made. Results from the analysis lend further evidence to the idea that spatial knowledge is stored and processed hierarchically and that individuals underrepresent information in large clusters. Consequently, the results have important implications for modeling any spatial behavior based on individuals' spatial information surfaces. In particular, the results cast further doubt on the validity of traditional large-scale spatial choice frameworks and lend support to the competing destinations hypothesis.     

The price for subway access: spatial econometric modelling of office rental rates in London

ABSTRACT

The relative location of commercial properties as the distance to the metro station as well as its absolute location as the address in the Central Business District (CBD) is expected to influence its valuation. We apply the advanced spatial econometric methodology, based on micro geo-located data, to the office rental transaction data from London in 2015. We use different spatial information on the local and global neighbourhood in order to estimate the spatial hedonic valuation model and test the strength of spatial spillovers and location effects on office fees. For offices in London in 2015, geographical factors may raise the price by 50%, as every next 100 m to the metro cost an additional 0.7 ? per ft² per year and the CBD location raises the cost for ca. 20 ? per ft² per year.     

空间相互作用模型中的目的地竞争效应 ——基于中国城市间铁路客流数据的实证研究

目的地竞争模型是空间相互作用领域的重要进展之一,但其有效性尚未得到一致认可,且缺乏基于中国的实证依据。基于中国2010年城市间铁路客流数据,采用目的地竞争模型进行实证分析,并与传统空间相互作用模型相比较,以检验目的地竞争模型在实际应用中的有效性。结果表明：① 空间结构对中国城市间铁路客流存在显著影响,目的地之间存在较强的竞争效应;② 目的地竞争模型的引入显著地减弱了距离衰减参数的空间自相关程度,较大程度上改善了传统空间相互作用模型的距离衰减参数标定偏误问题;③ 既有研究中在区域尺度下对传统空间相互作用模型（即重力模型）参数的标定及实证分析可能会存在偏误,目的地竞争模型这一改进模型具备应用价值。     

中国入境旅游热点城市知名度认知的空间相关性

 旅游知名度是游客旅游决策的主控影响因素之一,也是旅游目的地获取潜在客源的重要手段。以我国入境旅游热点城市知名度为研究内容,从入境游客认知视角,在大样本入境游客实证调查资料的支持下,运用Moran指数和相关性检验,分析了我国入境旅游热点城市知名度认知的空间相关性,检验结果显示：我国入境旅游热点城市知名度认知具有正向空间自相关属性,其空间分布特征具有明显的类型与等级差异。相关性检验结果显示：入境旅游热点城市知名度认知存在地域相邻高度相关;沿海入境旅游热点城市知名度认知存在高度相关;入境旅游热点城市知名度认知具有明显的类型或等级相关;国际焦点关注的入境旅游热点城市认知与其它城市之间存在高度相关。     

旅游目的地仿生学空间关系研究——以安徽省古村落西递、宏村为例

旅游目的地之间在多重因素的影响之下,空间关系总会呈现出有别于竞争与合作关系的其他形式。研究了旅游目的地空间关系的生态学解释和旅游生态位概念,在此基础上建立仿生学空间关系数学模型,构建旅游目的地仿生学空间关系研究体系,并将新体系实际应用于案例地西递、宏村,最后得出结论:宏村的整体竞争力水平高于西递,且两村于2006～2007年间达到稳定的竞争状态并一直延续到现在。案例分析证明了旅游目的地仿生学空间关系研究对于旅游规划与管理的理论和实践意义。     

A regionalization method for clustering and partitioning based on trajectories from NLP perspective

ABSTRACT

Regionalization attempts to group units into a few subsets to partition the entire area. The results represent the underlying spatial structure and facilitate decision-making. Massive amounts of trajectories produced in the urban space provide a new opportunity for regionalization from human mobility. This paper proposes and applies a novel regionalization method to cluster similar areal units and visualize the spatial structure by considering all trajectories in an area into a word embedding model. In this model, nodes in a trajectory are regarded as words in a sentence, and nodes can be clustered in the feature space. The result depicts the underlying socio-economic structure at multiple spatial scales. To our knowledge, this is the first regionalization method from trajectories with natural language processing technology. A case study of mobile phone trajectory data in Beijing is used to validate our method, and then we evaluate its performance by predicting the next location of an individual’s trajectory. The case study indicates that the method is fast, flexible and scalable to large trajectory datasets, and moreover, represents the structure of trajectory more effectively.     

基于要素流的长三角城市群空间布局

以长三角城市群30个地级市市区为研究单元,从要素流动的动态视角,建立要素流的数理模型,借助于ArcGIS软件,对长三角城市群内城市的经济流、交通流和信息流的强度和方向进行定量分析和评价,运用赋值法对3种要素流总量进行评分,并进行空间层级划分。得出核心区（包括上海、江苏沿江八市、环杭州湾地区等地市）和辐射影响区（位于长三角两翼的地市）。基于城市间的空间联系特征和城市群的空间层级结构,对长三角城市群的空间发展布局提出进一步引导和规划,即上海为核心,苏州、南京、杭州、合肥和徐州为区域性中心城市,构成以沪宁合、沪杭、沪徐和沪温为4个发展轴的反“K”字型的空间形态,依据城市区位和要素联系特征,将城市群进一步划分为6个城市组团,形成以中心城市为增长节点、以发展轴为联结线、以城市组团为发展域的“点―线―面”有序发展的空间互动格局。     

北京市建筑设计产业空间分布与区位选择

超大城市的城市空间结构调整需求日益迫切,城市内细分产业的空间分布及区位选择的研究也变得更加重要.以北京市为例,从微观视角分析了超大城市内部企业空间分布规律,以期为城市优化建筑设计产业空间结构提供理论支撑.运用核密度估计方法、Ripley's K(d)函数以及负二项模型,分析了建筑设计企业的空间分布及其区位选择的影响因素...     

重庆市主城区软件和信息技术服务企业空间格局演化与区位选择

基于重庆市工商企业注册数据,利用核密度分析、多距离空间聚类等空间分析方法,分析了重庆市主城区软件和信息技术服务企业2002—2017年的空间格局演化特征,并借助Tobit模型探讨其区位选择影响因素。研究表明：1）从现状分布看,距离城市中心4~12 km的城市中间圈层是重庆市主城区软件和信息技术服务企业分布的主要空间,并呈“双峰”圈层分布特征,空间分布表现为“一主两次”多中心格局,集聚特征显著。2）2002—2017年,企业分布出现郊区化现象的同时大量向距离城市中心6~8 km范围集中,圈层分布由“双峰”结构向“单峰”结构转变,空间格局由单中心分布演变为“一主两次”多中心格局,集聚强度与规模持续提高。3）产业集聚因素对重庆市主城区软件和信息技术服务企业的区位选择影响最为显著,其次是相关区位条件和市内交通因素的影响,对外交通和空间政策因素在企业区位选择中的作用未通过显著性检验。     

南京大学信息地理学的发展回顾与研究展望

信息地理学研究人类信息传播的地理学问题,包括信息传播的空间分布规律、空间结构及其空间影响,特别是对人类社会、经济空间组织的影响以及由于虚拟现实技术带来的地理学问题等。南京大学系统性的展开了信息地理学的研究,并深植于传统的优势地理学科,形成信息时代空间的形态、信息时代的空间效应、数字与信息城市、信息技术与旅游、信息时代的居民行为活动5个重点研究领域。面向全新的移动信息时代,以及信息地理学在社会文化及人文方面的研究不足,南京大学信息地理学面临五大发展趋势:信息技术深入影响下的城市空间研究、移动信息技术作用下的行为地理研究、信息技术与旅游结合的地理学研究、移动信息时代的智慧城市研究、物联网的地理学响应研究。     

基于POI数据的大型零售商业设施空间布局与业态差异——以广州市为例

以广州市为例,基于大型零售商业设施POI（Point of Interest）数据,运用空间计量分析方法,对大型零售商业设施总体及不同业态的空间分布及其影响因素进行了探究。结果表明：总体上,大型零售商业设施空间分布呈现出由内向外扩散的多中心圈层式结构,沿交通干道生长和连片蔓延趋势明显,中心城区设施临近轨道交通分布指向性突出。不同业态分布差异明显,大型购物中心和商场主要分布在中心城区及外围城区次中心,大型综合超市由中心城区内部向边缘区域扩散趋势明显,大型专业店主要分布在中心城区的边缘区域。影响因素方面,人口、经济和租金因素对其总体分布影响显著为正。不同业态所受影响因素有所不同,除人口等传统影响因素外,与生活中心、产业园或专业市场的临近性也会分别对大型综合超市和大型专业店的空间分布造成显著影响。     

Delivering time-evolving 3D city models for web visualization

ABSTRACT

Studying and planning urban evolution is essential for understanding the past and designing the cities of the future and can be facilitated by providing means for sharing, visualizing, and navigating in cities, on the web, in space and in time. Standard formats, methods, and tools exist for visualizing large-scale 3D cities on the web. In this paper, we go further by integrating the temporal dimension of cities in geospatial web delivery standard formats. In doing so, we enable interactive visualization of large-scale time-evolving 3D city models on the web. A key characteristic of this paper lies in the proposed four-step generic approach. First, we design a generic conceptual model of standard formats for delivering 3D cities on the web. Then, we formalize and integrate the temporal dimension of cities into this generic conceptual model. Following which, we specify the conceptual model in the 3D Tiles standard at logical and technical specification levels, resulting in an extension of 3D Tiles for delivering time-evolving 3D city models on the web. Finally, we propose an open-source implementation, experiments, and an evaluation of the propositions and visualization rules. We also provide access to reproducibility notes allowing researchers to replicate all the experiments.     

基于社会网络理论的旅游系统空间结构优化研究

基于交通网络和旅游资源禀赋的旅游系统空间结构研究具有一定欠缺性。借鉴Shih研究成果,基于旅游者行走游线和旅游流量,分析社会网络理论的程度中心性、接近中心性和中介中心性3个指标作为衡量区域旅游进出通道和中心性、可进入性和旅游集散中心的意义,将目的地划分为区域旅游核心区、缓冲区和边缘区,并对区域旅游分工和设施配备要求提供理论指导。旅游核心区应配备旅游中心、信息中心和口岸交通设施,旅游缓冲区应加强旅游景区建设,旅游边缘区在加强景区建设的同时还应强化宣传工作。探讨旅游系统空间结构的影响因素及其优化措施,并对福建进行实证研究。     

北京西北地区景观格局优化

Supported by the technologies of remote sensing (RS) and geographical information system (GIS), we chose northwest of Beijing as a study area and gave priority to understanding of the spatial-temporal characteristics of landscape pattern change through visually interpreted Landsat TM images of 1989, 1996 and 2005. It is believed that there were a series of landscape ecological problems caused by city expansion: landscape ecological connectivity was low; landscape structure was simplified; the fragmentation of green land patch was more obvious on the plain areas, moreover, spatial distribution of green land was unbalanced. For this reason, this study adopted accumulative cost distance model, combined with ecosystem services and spatial interactions of landscape types, analyzed the spatial difference of the ecological function and the compactness of landscape structure in the study area, and further discussed the landscape pattern optimization proposal. We find that it is essential to protect and establish ecological sources, to establish urban ecological corridors, and to establish ecological nodes at the landscape ecological strategic positions so as to intensify spatial relationships among landscape elements and maintain continuity of landscape ecological process and pattern in the course of city expansion. The methods and final results from this study are expected to be useful for landscape ecological planning in Beijing region. Foundation: Program for New Century Excellent Talent in the University, No.NCET-06-0122; The National Water Special Project, No.2008ZX07526-002-02; Specific Financial Funds Project of Beijing Academy of Science and Technology (BJAST), Platform Construction for Typical Contaminated Soil Remediation Technology of Beijing (2008A-1); Plan Support for Innovative Team (2008A-6) of BJAST; NSFC, No.30871964; BJNSFC, No.4073036 Author: Yue Depeng (1963–), Ph.D and Associate Professor, specialized in desertification control, landscape ecology and land evaluation.     

基于GIS与地理探测器的旅游地空间分布格局及驱动力分析——以贵州为例

以贵州旅游地空间数据为基础,运用平均最邻近、地理探测器等方法,研究贵州1990—2018年4个时间截面旅游地空间格局,分析该时期旅游重心演化进程,探讨旅游地与地理环境、社会经济要素间关系并开展驱动力分析。结果表明：① 4个时期旅游地均为凝聚状态分布,总体呈现点、团向面状、带状聚集转变;② 全省旅游收入演化比客流量演化更为敏感和剧烈,二者均以经向变幅为主导,且呈现西部比重大于东部的态势;③ 旅游地在海拔高程垂向上表现为近似正态分布,碳酸盐岩类区旅游地资源多为自然类景点、碎屑岩类区主要表现为民族文化类景点的聚集分布,断层线5 km缓冲区范围内分布90%左右的旅游地、其空间展布总体与断层线走向吻合,三叠、寒武及二叠地层是旅游地的主要分布区,旅游地沿省道特征分布明显;④ 全省旅游地分布主要受旅游地到河流沟谷距离、县级行政中心距离、高速公路直线距离、陆上交通路网密度、高速公路路网密度、断层线距离、河流沟谷密度等7项因子驱动;而距河流沟谷、距县级行政中心、距高速公路直线距离以及交通路网密度水平等因子的组合效应集中控制了当前贵州旅游地空间分布的总体格局。     

Regions and western europe

Abstract

Regional geography is undergoing important changes in its method of study in order to achieve a greater degree of relevancy in the context of a dynamic global system. Effective knowledge of regional identities can no longer be confined to facts that establish patterns, it also demands a critical appreciation of processes and spatial interrelationships. Western Europe is taken as a case study to reflect this new approach to regional geography, Despite the complexity of this major world region, two basic models are proposed as a basis on which to develop detailed study. Essential patterns of Western Europe are established through six generalizations: location; quality of the environment; industrialization; urban development and trade; population and culture; and the European Community/European Union. Processes and interrelations are reviewed through a core-periphery model. Changing regional patterns are highlighted which suggest a movement away from a national focus for regional study to one which is more international.     

中国城市间金融网络的空间演化及其影响因素

基于中国金融业企业网络数据和链锁网络模型建立城市网络,在解析金融网络空间格局的基础上,定量识别与测度了金融网络的影响因素,并探讨了关键因素的作用机理。研究发现：中国城市间金融网络的中心性体系呈现空间指向和路径依赖的特征,链接关系呈现纵深关联和层级分化的特征,核心―边缘结构呈现逐渐深化和有序分布的特征;关键资源、空间距离、区位条件和历史基础是影响金融网络空间格局的关键因素;择优链接、地理邻近和空间集聚是金融网络生长发育的动力机制。在网络发展环境下,中国城市化政策需要做出相应调整,支持城市在金融网络中选择差异化的发展路径,充分发挥金融网络对城市经济增长的供给引导功能,同时在更大空间尺度上推动城市之间的网络协作。     

Integration of air pollution data collected by mobile sensors and ground-based stations to derive a spatiotemporal air pollution profile of a city

ABSTRACT

Air pollution has become a serious environmental problem causing severe consequences in our ecology, climate, health, and urban development. Effective and efficient monitoring and mitigation of air pollution require a comprehensive understanding of the air pollution process through a reliable database carrying important information about the spatiotemporal variations of air pollutant concentrations at various spatial and temporal scales. Traditional analysis suffers from the severe insufficiency of data collected by only a few stations. In this study, we propose a rigorous framework for the integration of air pollutant concentration data coming from the ground-based stations, which are spatially sparse but temporally dense, and mobile sensors, which are spatially dense but temporally sparse. Based on the integrated database which is relatively dense in space and time, we then estimate air pollutant concentrations for given location and time by applying a two-step local regression model to the data. This study advances the frontier of basic research in air pollution monitoring via the integration of station and mobile sensors and sets up the stage for further research on other spatiotemporal problems involving multi-source and multi-scale information.     

Spatial interpolation using conditional generative adversarial neural networks

ABSTRACT

Spatial interpolation is a traditional geostatistical operation that aims at predicting the attribute values of unobserved locations given a sample of data defined on point supports. However, the continuity and heterogeneity underlying spatial data are too complex to be approximated by classic statistical models. Deep learning models, especially the idea of conditional generative adversarial networks (CGANs), provide us with a perspective for formalizing spatial interpolation as a conditional generative task. In this article, we design a novel deep learning architecture named conditional encoder-decoder generative adversarial neural networks (CEDGANs) for spatial interpolation, therein combining the encoder-decoder structure with adversarial learning to capture deep representations of sampled spatial data and their interactions with local structural patterns. A case study on elevations in China demonstrates the ability of our model to achieve outstanding interpolation results compared to benchmark methods. Further experiments uncover the learned spatial knowledge in the model’s hidden layers and test the potential to generalize our adversarial interpolation idea across domains. This work is an endeavor to investigate deep spatial knowledge using artificial intelligence. The proposed model can benefit practical scenarios and enlighten future research in various geographical applications related to spatial prediction.