基于地理格网分级法提取的中国传统村落空间分布

中国传统村落真实地记录了建筑、民俗民风和较原始的民居空间形态,是千百年来人类与自然和谐共存的生活基础。随着城市化进程加快,传统村落的存续受到了很大的挑战,为了更好地保护利用传统村落,对其进行多视角的研究尤为重要,除了微观层面的具体保护措施外,还应加强宏观层面的空间分布研究。地理格网是表达复杂地理现象、综合分析自然与人文数据、模拟地理系统功能与行为的基本方法。本文从百度影像地图提取村落坐标,并截取遥感影像作为村落点数据的判断补充,在此基础上,运用地理格网的分级法,对国家公布的三批中国传统村落(2555个)数据进行空间格局分析,结果表明：这些村落主要分布在中国东南部,如落在一级格网Ⅰ18、Ⅰ19就分别有586、781个村落,占三批传统村落总数的96.6%。通过对二级和三级格网的村落分布离散性进行分析,使传统村落的空间分布数据结构更加系统和清晰有序,为合理构建其数据库提供了框架基础。     

Effects of demand estimates on the evaluation and optimality of service centre locations

Public service systems, such as emergency health care, police or fire brigades, are critical for day-to-day functioning of the society. To design and operate these systems efficiently much data needs to be collected and properly utilised. Here, we use the OpenStreetMap (OSM) data to model the demand points (DPs), which approximate the geographical location of customers, and the road network, which is used to access or distribute services. We consider all inhabitants as customers, and therefore to estimate the demand, we use the available population grids. People are changing their location in the course of the day and thus the demand for services is changing accordingly. In this paper, we investigate how the used demand estimate affects the optimal design of a public service system. We calculate and compare efficient designs corresponding to two demand models, a night-time demand model when the majority of inhabitants rest at home and the demand model derived from the 24-hour average of the population density. We propose a simple measure to quantify the differences between population grids and we estimate how the size of differences affects the optimal structure of a public service system. Our analyses reveal that the efficiency of the service system is not only dependent on the placement strategy, but an inappropriate demand model has significant effects when designing a system as well as when evaluating its efficiency.     

Interaction between scale and scheduling choices in simulations of spatial agents

Spatial simulations are a valuable tool in understanding dynamic spatial processes. In developing these simulations, it is often required to make decisions about how to represent features in the environment and how events unfold in time. These spatial and temporal choices have been shown to significantly alter model outcomes, yet their interaction is less well understood. In this paper, we make use of a simple group foraging model and systematically vary how features are represented (cell size of the landscape) as well as how events unfold in time (order in which foragers take action) to better understand their interaction. Our results show similar nonlinear responses to changes in spatial representation found in the literature, and an effect of the order in which agents were processed. There was also a clear interaction between how features are represented and how events unfold in time, where, under certain environmental representations results were found to be more sensitive to the order in which individuals were processed. Furthermore, the effects of feature representation, scheduling of agents, and their interaction were all found to be influenced by the heterogeneity of the spatial surface (food), suggesting that the statistical properties of the underlying spatial variable will additionally play a role. We suggest that navigating these interactions can be facilitated through a better understanding of how these choices affect the decision landscape(s) on which agents operate. Specifically, how changes to representation affect aggregation and resolution of the decision surface, and thereby the degree to which agents interact directly or indirectly. We suggest that the challenges of dealing with spatial representation, scheduling, and their interaction, while building models could also present an opportunity. As explicitly including alternate representations and scheduling choices during model selection can aid in identifying optimal agent–environment representations. Potentially leading to improved insights into the relationships between spatial processes and the environments in which they occur.     

Opportunistic natural experiments using digital telemetry: a transit disruption case study

ABSTRACT

In the past decade society has entered a technological period characterized by handheld computing that supports input and processing from numerous sensors. Today’s mobile phones offer the ability to integrate input from sensors monitoring various external and internal sources (e.g., accelerometer, magnetometer, microphone, GPS, wireless Internet, and Bluetooth). Furthermore, these raw inputs can be integrated and processed in ways that can offer novel representations of human behaviour. As a result, new opportunities to examine and better understand human spatial behaviour are available; one such application is the constant monitoring of a group of people over an extended period of time. Such a research setting lends itself to natural experiments that emerge as a result of regular and on-going observations. We report here on the observation of a natural experiment that took place in the context of a month-long monitoring study of 28 participants using mobile phone-based ubiquitous sensor monitoring. The implications for public health and transportation planning are discussed.     

Estimating spatial efficiency using cyber search,GIS, and spatial optimization: a case study of fire service deployment in Los Angeles County

ABSTRACT

The efficiency of public investments and services has been of interest to geographic researchers for several decades. While in the private sector inefficiency often leads to higher prices, loss of competitiveness, and loss of business, in the public sector inefficiency in service provision does not necessarily lead to immediate changes. In many cases, it is not an entirely easy task to analyze a particular service as appropriate data may be difficult to obtain and hidden in detailed budgets. In this paper, we develop an integrative approach that uses cyber search, Geographic Information System (GIS), and spatial optimization to estimate the spatial efficiency of fire protection services in Los Angeles (LA) County. We develop a cyber-search process to identify current deployment patterns of fire stations across the major urban region of LA County. We compare the results of our search to existing databases. Using spatial optimization, we estimate the level of deployment that is needed to meet desired coverage levels based upon the location of an ideal fire station pattern, and then compare this ideal level of deployment to the existing system as a means of estimating spatial efficiency. GIS is adopted throughout the paper to simulate the demand locations, to conduct location-based spatial analysis, to visualize fire station data, and to map model simulation results. Finally, we show that the existing system in LA County has considerable room for improvement. The methodology presented in this paper is both novel and groundbreaking, and the automated assessments are readily transferable to other counties and jurisdictions.     

中国省际人口迁移的多边效应机制分析

区际人口迁移不仅与迁出地和目的地的要素特征以及距离有关,而且还受到周边迁移流的影响.基于网络自相关理论,利用"六普"省际人口迁移数据和相关统计资料,在重力模型的基础上考虑迁移流之间可能存在的几种空间依赖形式,构建中国省际迁移流的空间OD模型,初步揭示区域经济社会等因素及其空间溢出效应对省际人口迁移的影响,并就区域要素变化对整个省际人口迁移系统产生的"连锁反应"进行了模拟.结果表明:① 中国省际迁移流之间存在显著的网络自相关效应.目的地和迁出地的自相关效应皆为正,导致迁入和迁出流的空间效仿行为;迁出地和目的地周边则出现负的自相关效应,导致迁移流的空间竞争行为;② 区域经济社会等因素通过网络空间关系对周边地区产生的多边溢出效应导致迁移流在空间上集聚.其中,距离衰减效应位居各要素之首,其溢出效应进一步加剧距离的摩擦作用;对目的地而言,区域工资水平和迁移存量超过GDP的影响并产生正的溢出效应,促进周边地区吸引更多的外来人口;对迁出地而言,人口规模和迁移存量产生正的溢出效应,推动周边地区人口外迁;③ 区域要素变化潜在地对整个省际人口迁移系统产生一系列"连锁反应",震荡中心及其周边区域的迁移流波动较大.江苏省GDP增长5%的模拟结果表明,江苏迁往全国其他省份的人口数量都有不同程度地减少,而其他省份入迁人口均有所增加.相对而言,江苏周边省份的迁入或迁出流受到的波动较大,偏远省份波及较小,这是传统的重力模型所无法解释的.     

中国装备制造业产学研合作创新网络初探

产学研合作是区域创新的主要途径和重要来源.以中国装备制造产业为例,基于中国知识产权局1985-2012年间的合作发明专利数据,借助SPSS,UCINET,ArcGIS等定量分析工具,对中国装备制造产业合作网络的创新主体结构,空间结构及其演变,创新合作的空间尺度的影响因素进行了分析.研究发现,民营企业,高校在中国装备制造产业创新网络中的地位不断上升,数量不断增加,且已经成为重要的创新源泉;市域空间合作成为发达地区城市产学研创新合作最重要的空间单元,国家空间是欠发达地区城市产学研创新合作的主要空间载体;理工科高校等科技资源的空间集聚态势是导致创新网络层级特征的主要因子,科技资源富集的行政中心如直辖市,省会城市等发达城市成为最重要的资源集聚地,创新源泉和创新合作对象.     

中国“四纵四横”高铁网络可达性综合评估与对比

传统测算可达性的方法较为单一,缺乏多层面、多角度的综合研究和对比分析。基于时间、经济与重心视角利用可达性模型并结合ArcGIS 空间分析手段探究高铁通车前后沿线城市可达性的动态变化;利用综合变异系数、层级分析法和改进的哈夫模型分析高铁可达性空间演变特征及规律,构建高铁可达性评价体系并结合熵权法定量评估各高铁可达综合实力。研究表明：京广、京沪可达综合实力最强,沪昆、杭福深次之,沪汉蓉、哈大、青太再次之,郑西、兰新可达实力最弱;全国高铁可达性强弱变化呈明显地带性规律,东、中部高铁可达性强于东北,东北强于西部,纵向高铁强于横向;经济潜力的增加率明显高于加权平均旅行时间的减少率,可达性重心偏移驱使不同等时圈蔓延交叠;高铁以“核心—核心”逐步向“核心—网络”空间链接模式过渡,产生上海、北京、广州、深圳4 个高铁“国家服务中心”,天津、武汉、重庆等6 个“大区域服务中心”,形成“多中心”高铁服务格局以及日益庞大而复杂的高铁特质空间集群;高铁网络影响下的中国区域空间格局的渐变与重塑日趋复杂,“T”型轴带呈现出由空间极化向空间均衡转变,跨城流动性特征突显,加速空间对接与同城化进程。     

中国城市流动人口户籍迁移意愿的空间格局及影响因素——基于2012年全国流动人口动态监测调查数据

基于2012年全国流动人口动态监测调查数据和相关统计年鉴数据,对中国地级以上城市流动人口的户籍迁移意愿、空间格局及影响因素进行了系统分析。研究发现,中国城市流动人口户籍迁移意愿的整体水平并不高;等级高、规模大的城市流动人口的户籍迁移意愿高,而等级低、规模小的城市流动人口的户籍迁移意愿低;沿海城市群流动人口的户籍迁移意愿高,其他城市流动人口的户籍迁移意愿低,但内陆部分省会城市和交通区位与资源禀赋较好的中小城市也已经形成了一批流动人口的户籍迁移意愿高值区。中国城市流动人口户籍迁移意愿的空间分布特征受到流入城市和流动人口自身双重力量的影响,流入城市因素的正向影响作用大于流动人口自身因素。其中,流入城市的社会、经济发展水平和流动人口在流入城市的社会融合程度是核心要素,对城市流动人口户籍迁移意愿的提升具有正向的促进作用,而流动人口过于集中在次级劳动力市场的就业特征和较高的家庭财富与收入对户籍迁移意愿的提升却具有显著的抑制作用。最后,提出了相关政策启示。