河南省县域学校体系规模、可达性演化特征及其分异

县域义务教育学校体系规模的合理布局、适龄儿童入学可达性对义务教育公平和均衡发展具有重要意义。采用基尼系数、齐夫指数等方法对泌阳县2004—2016年县域小学学校体系规模空间演化特征、就学可达性变化及其空间分异进行了实证研究。结果表明：研究区县域小学规模两极分化严重，学生和教师的城乡不平衡指数大大增加，首位学校以及位序靠前的学校垄断性增强，学校规模从均衡发展演化为非均衡状态；2016年入学距离在2 km范围内的自然村相比2004年略有降低，距最近完全小学在2 km范围内的自然村出现大幅降低，完全小学大幅减少，服务范围增大，覆盖度出现显著降低；2016年各乡镇学生、教师基尼系数显著增加，全县范围空间分异规律基本一致；相比2004年，2016年地形高度、距乡镇政府所在地距离对自然村入学距离影响显著增强，特别是对最近完全小学入学距离的影响更大。     

Clusters of Nonmedical Exemptions to Vaccination in Illinois K–12 Schools

Vaccination rates in Illinois schools are decreasing as more parents opt for nonmedical exemptions (NMEs). At the local scale, higher levels of exemptions affect herd immunity levels. Few studies have previously conducted or proposed methods to conduct local-scale spatial and temporal cluster pattern analysis. This study used vaccination exemption data from the Illinois School Board of Education’s annual Immunization School Survey for the 2003–2004 and 2013–2014 academic years. The Getis–Ord General G statistic was used to identify cluster detection by individual vaccine at the school level. The Getis–Ord Gi* statistic was used with two different parameter models to identify hot and cold spots. This study found that NMEs are highly clustered. More clusters of high and low NMEs were identified for the 2013–2014 academic year than for 2003–2004. The percentages of schools that were neither hot nor cold averaged 94.0 percent for the 2003–2004 school year and 78.7 percent for the 2013–2014 school year. NME rates in Illinois are rising. The increase in hot and cold spots is evidence that the polarity of vaccination choice is growing. As vaccination exemption rates continue to polarize U.S. society, it is essential for public health efforts to monitor and conduct local-level studies. Key Words: antivaccination, hot spot analysis, medical geography, nonmedical exemption, spatial statistics.     

An Alternative Classification Scheme for Uncertain Attribute Mapping

The reality of uncertain data cannot be ignored. Anytime that spatial data are used to assist planning, decision making, or policy generation, it is likely that error or uncertainty in the data will propagate through processing protocols and analytic techniques, potentially leading to biased or incorrect decision making. The ability to directly account for uncertainty in spatial analysis efforts is critically important. This article focuses on addressing data uncertainty in one of the most important and widely used exploratory spatial data analysis (ESDA) techniques—choropleth mapping—and proposes an alternative map classification method for uncertain spatial data. The classification approach maximizes within-class homogeneity under data uncertainty while explicitly integrating spatial characteristics to reduce visual map complexity and to facilitate pattern perception. The method is demonstrated by mapping the 2009 to 2013 American Community Survey estimates of median household income in Salt Lake County, Utah, at the census tract level.     

Using a Bayesian estimator to combine information from a cluster analysis and remote sensing data to estimate high-resolution data for agricultural production in Germany

In Germany, a county-resolution data set that consists of 35 land-use and animal-stock categories has been used extensively to assess the impact of agriculture on the environment. However, because such environmental effects as emission or nutrient surplus depend on the location, even a county resolution might produce misleading results. The aim of this article is to propose a Bayesian approach which combines two sorts of information, with one being treated as defining the prior and the other the data to form a posterior, used to estimate a data set at a municipality resolution. We define the joint prior density function based on (i) remote sensing data, thus accounting for differences in county data and missing data at the municipality level, and (ii) the results of a cluster analysis that was previously applied to the micro-census, whereas the data are defined by official statistics at the county level. This approach results in a fairly accurate data set at the municipality level. The results, using the proposed method, are validated by the national research data centre by comparing the estimates to actual observations. The test statistics presented here demonstrate that the proposed approach adequately estimates the production activities.     

AN ASSESSMENT OF THE ADEQUACY OF THE SOLAR RADIATION DATA NETWORK FOR THE CONTIGUOUS UNITED STATES

Relationships between the coefficient of variability and the logarithm of extrapolation distance are established by an assessment of the spatial coherence of solar radiation. Equations for different confidence levels and daily and monthly time intervals are presented. These relationships are utilized in an assessment of the adequacy of the solar radiation data network in the contiguous United States. Results indicate that the present network maintained by the federal government provides adequate coverage on a monthly basis for all but 5% of the area for an error tolerance of ±10% at a 90% confidence level. However, on a daily basis, very inadequate spatial coverage is provided.     

农村居民点耕作距离空间分布特征估测分析

基于探索性空间数据分析(ESDA)思想,提出一种耕作距离空间估测方法,定量分析农村居民点耕作距离的分布格局与分异特征。以广东省阳山县为案例,研究表明：① 耕作距离与耕地质量及地形等别存在一定的相关性,耕作半径愈小,地形愈低平的地区,居民点与较优的耕地愈集中;② 耕作距离在空间上呈现不均衡状态,随着距离尺度的增加,耕作距离的变化由低值聚类转为高值聚类状态,表明研究区耕地格局随耕作距离尺度上升变得更加分散;③ 估测方法考虑了农村居民点与耕地之间的关系,可以有效分析耕地与居民点之间的空间分布随耕作距离变化的规律,对农村居民点调整、高标准农田建设等工作的合理开展具有参照和一定指导意义。     

2000—2015年中国高学历人才分布格局及其影响机制

基于2000年、2010年中国人口普查分地级及以上行政区数据和2015年中国各省份1%人口抽样调查数据，采用变异系数、泰尔系数、基尼系数测度中国高学历人才比重分布的空间不均衡程度，并采用面板数据Tobit随机效应模型，识别影响高学历人才比重空间分布的主要因素。结果表明：① 中国的人才比重分布表现出极大的不均衡性，人才比重的高低与城市等级密切相关，主要表现在直辖市、省会城市、计划单列市等行政区吸引了大量人才，而普通地级城市人才比重相对较低；② 10 a间人才比重的变异系数、基尼指数和泰尔指数均有所下降，表明人才比重分布的空间不均衡程度有所下降；③ 平均工资水平、生活成本、城市等级、每万人高校在校学生数、每万人医院卫生院床位数、人均科学事业、教育事业经费与人才比重呈正相关，中学生师比、万人互联网用户数与人才比重呈负相关，失业率、单位面积二氧化硫排放量、绿地率对人才比重没有影响。     

ArcGIS与SAS地统计分析方法应用于美国县区人口加权月温度估算的比较(英文)

气温变化对人群健康有重要的影响。通过对美国县区人口加权的月平均温度的准确估计可以用于气温与人群健康行为以及疾病的关联关系研究,如基于以县区为单位的抽样或者报告数据。针对气温的估计,多数学者都采用ArcGIS软件,很少使用SAS这一统计软件。本文比较了两种地统计模型的性能,并在同一个CITGO平台上采用ArcGIS9.3和SAS9.2工具软件估算全美48个州县区月平均温度。来自全美5435个气温监测站点2007年1-12月的平均温度和站点的海拔高度被用于估算县区人口中心点的温度,其中海拔数据是作为协变量。通过调整决定系数R2、均方误差、均方根误差和处理时间等指标来比较模型的效能。在ArcGIS中独立验证预测准确性在11个月中都达到90%以上,SAS中12个月均达到90%以上。与ArcGIS协同克里格相比,SAS协同克里格插值能获得更高的准确性和较低的偏差。两个软件包对于县区水平的气温估计值呈现正相关(调整R2在0.95-0.99之间);通过引入海拔高度作为协变量,使准确性和精确性都得以改善。两种方法对于美国县区层面的气温估计都是可靠的,但ArcGIS在空间数据前期处理和处理时间上的优势,尤其在涉及多年或者多个州的项目中是软件选择上的重要考虑。     

黄河流域县域交通供给格局及其空间适配性

分析黄河流域交通供给水平特征及其与经济社会发展的空间适配性,对交通资源优化配置、提升交通供需适配性具有重要参考。论文基于黄河流域现状综合交通运输与经济社会基础数据,分析了黄河流域县域交通优势度分异特征,并探讨了交通与县域发展质量的空间适配性。结果表明：黄河流域县域交通优势度有待提升,在人口与GDP方面呈现一定错配特征;高优势度县域集中分布在济南—郑州—西安—兰州通道沿线。黄河流域交通供给均衡性水平高于经济社会差异;样带交通优势度呈现陇海—兰新“一”字型样带>“几”字湾样带>南北纵向“1”字型样带的分布态势,固定资产投资、产业结构、城镇化、经济基础是影响黄河流域县域交通优势度的主要因素。“几”字湾样带和南北纵向“1”字型样带还受到高程、坡度等自然因素的制约;流域40.05%的县域处于初级适配状态,38.78%的县域为交通劣势型,集中在青藏高原、黄土高原沟壑区和内蒙古中西部地区,亟需提升交通设施对该类县域经济社会发展的支撑与保障能力。     

广州长者饭堂供需匹配的类型、差异及调整思路

以广州市长者饭堂为研究对象,采用百度地图API、居民点POI、广州市为老综合服务平台等多源数据,综合考虑可达性和运营可行性,并对两步移动搜索法进行改进,通过转换测度结果和过程变量,刻画长者饭堂的供需匹配情景。研究发现：1）广州长者饭堂在“行政单元全覆盖”推广下供需空间匹配度六成合格,形成平衡型、剩余型和短缺型的数量“631”结构;2）供给和需求的主体优势、空间分布错位,需求获取优势高于供给,空间呈现“内低外高—单中心圈层”供给结构与“南强北弱—多核网状”需求结构;3）出行优势中心两级分化,成为剩余型和短缺型的主要聚集地。主核心片内出现“老城中心洼地”和“黄埔高地”,外围行政区出现多个剩余小高地或短缺谷地。最后,为精准干预长者饭堂网点布局,提出存量提升、配对整合、存量精简等适宜不同空间关联条件的不匹配型优化思路,优先考虑强空间关联的剩余型和短缺型街道配对整合;以及直接提升、主动提升、被动提升的匹配型提升思路,实现广州长者饭堂全覆盖全匹配目标。     

中国普通高中教育均等化及障碍因素分析

以中国31个省级行政单元为研究对象,从教育背景、教育投入、教育过程和教育结果4个方面构建指标体系,采用集对分析模型测度各省份普通高中教育均等化水平,运用障碍度分析法揭示阻碍各省份普通高中教育均等化水平提升的主要因素。研究表明：从综合均等度来看,普通高中教育均等化水平较高的省份集中在环渤海地区和江浙沪地区,其中北京市最高,河南省最低。从空间格局来看,各省份普通高中教育均等化水平有显著差异,总体上呈现“东-西-中”递减趋势。分析表明,P1（高中阶段毛入学率）、S3（高级职称专任教师比例）、S2（本科学历及以上专任教师比例）、P2（学校数量/万名学生）是影响各省份普通高中教育均等化水平提升的主要因素。     

大连市初中教育消费者的社会空间结构研究

基于消费者差异的特色均等化是义务教育未来发展的重要指向。运用因子生态分析法对大连市域范围内189所初级中学学生的问卷调查数据进行分析,提取出5个主因子并划分为5类初中教育消费者,得出大连市初中教育消费者在空间上呈现出职业的城乡分化、受教育水平的南北差异等多重分异特征。然后采用聚类分析方法和集中度指数将所研究的初中教育消费者所在学校划分为4类,即高学历高收入家庭子女学校、城市中上收入家庭子女学校、农民及外出务工人员子女学校和工人及办事人员子女学校。最后根据不同类型初中教育消费者所在学校空间上的分布特征,归纳出大连市初中教育消费者社会空间结构的圈层分布特征。     

基于空间错位理论的河南省旅游发展区域差异研究

以河南省18个地市为研究对象,采用旅游资源单体数目和旅游接待人数作为空间错位的测度指标,构建旅游空间错位指数的测度模型,对河南省旅游空间错位的程度进行了定量研究。结果表明,河南省18个地市均存在不同程度的旅游空间错位现象,其中,郑州、开封、濮阳、三门峡、信阳5城市存在正向错位现象,而其他13个城市表现出负向错位现象。根据旅游空间错位程度,可将18个地市分为高错位区域、中错位区域、低错位区域3种类型,并针对错位程度不同的区域提出相应对策建议。     

An ensemble approach to space–time interpolation

The availability of spatial data on an unprecedented scale as well as advancements in analytical and visualization techniques gives researchers the opportunity to study complex problems over large urban and regional areas. Nevertheless, few individual data sets exist that provide both the requisite spatial and/or temporal observational frequency to truly facilitate detailed investigations. Some data are collected frequently over time but only at a few geographic locations (e.g., weather stations). Similarly, other data are collected with a high level of spatial resolution but not at regular or frequent time intervals (e.g., satellite data). The purpose of this article is to present an interpolation approach that leverages the relative temporal richness of one data set with the relative spatial richness of another to fill in the gaps. Because different interpolation techniques are more appropriate than others for specific types of data, we propose a space–time interpolation approach whereby two interpolation methods – one for the temporal and one for the spatial dimension – are used in tandem to increase the accuracy results.

We call our ensemble approach the space–time interpolation environment (STIE). The primary steps within this environment include a spatial interpolation processor, a temporal interpolation processor, and a calibration processor, which enforces phenomenon-related behavioral constraints. The specific interpolation techniques used within the STIE can be chosen on the basis of suitability for the data and application at hand. In this article, we first describe STIE conceptually including the data input requirements, output structure, details of the primary steps, and the mechanism for coordinating the data within those steps. We then describe a case study focusing on urban land cover in Phoenix, Arizona, using our working implementation. Our empirical results show that our approach increased the accuracy for estimating urban land cover better than a single interpolation technique.     

武汉市不透水地表时空格局分析

根据植被-不透水地表-土壤（V-I-S）模型,以武汉市2002、2009、2015年3期Landsat影像为数据源,利用线性光谱混合模型进行亚像元分解获取不透水地表比例的空间分布。在此基础上采用梯度分析、景观格局指数等方法对研究区内不透水地表空间格局及变化进行分析。得到以下结论：武汉市2002、2009、2015年的平均不透水地表比例分别是27.53%,34.65%,40.51%,呈不断增长的趋势。主城区的不透水地表比例明显高于新城区,但新城区不透水地表比例增加幅度大于主城区。武汉市不透水地表主要沿长江、汉江两条轴线分布,随着与城市中心距离的增大,不透水地表比例递减后趋于稳定,三环线内4 km和三环外10 km范围是平均不透水地表比例增量最大的圈层。2002~2015年,武汉市由自然地表与极低盖度等级占主导的景观格局逐渐演变为以中高盖度不透水盖度等级占主导。     

公平导向的学校分配空间优化——以北京石景山区为例

空间优化正从地理学的一个研究方向成为学科分支,但公平导向的公共服务空间分配优化研究还较薄弱。学区配置是这方面的一个典型问题,受到社会的广泛关注。针对“多校划片”的随机入学方式,以各居住小区所获得的教育质量期望值的方差来定义入学指标空间分配的公平程度,构建了以该方差最小化为目标、包含最大上学距离约束的二次规划模型,探求各小区对所获得指标进行随机抽签的情景下,各校入学指标在各小区的公平最大化分配,并以北京市石景山区为案例进行了应用研究。结果表明,在维持学校布局和师资配置现状的前提下,与“就近入学”相比,公平最大化的“多校划片”能以有限的上学距离代价,显著降低教育分配的空间差异。在最大上学距离为5 km的约束下,各小区教育质量期望值的方差降幅高达99%,教育质量期望值有所提升的小区或学生比例高达约2/3;付出的上学距离代价较显著但可接受,平均上学距离增加到3.99倍,达到3.20 km,仍低于案例区实际平均上学距离。当模型的最大上学距离参数从5 km逐步增至8 km时,教育公平的改善呈指数增长,平均上学距离呈算数增长;当距离为7 km时,各小区教育质量期望值的方差趋近于0,可基本实现上学机会的空间均等化。本文进一步讨论了优化结果对入学政策的启示。     

关中平原城市群植被覆盖的时空特征与影响因素

植被是全球及区域生态系统环境变化的重要指标,也是对人类社会活动有重要贡献的资源之一。为了研究关中平原城市群不同区域植被覆盖变化对自然和人文因子的响应,以划分为三个区域的植被作为研究对象,选取2000—2017年MODIS-NDVI遥感数据,运用趋势分析、探索性空间数据分析与地理探测器等方法,从时序演进与空间分布方面研究了18 a内植被覆盖的演化及分布特征,定量分析影响植被覆盖的主导因子。遥感数据要通过投影转换、拼接、最大值合成等方法进行处理,再运用Python程序进行影像批量裁剪,将遥感数据和气象数据进行分区统计,最后对该处理数据进行讨论研究。结论表明：（1） 研究期内关中平原城市群植被覆盖呈显著上升趋势,NDVI平均值增速为0.077·（10 a）^–1,阶段性变化特征明显,其中2005—2007阶段和2011—2013阶段极显著增加,最大上升速率达到了0.05·a^–1。（2） 空间上总体呈现“南高北低”的分布特征,研究区总体得到改善;高值区主要分布在南部秦岭北坡,受气候因子的影响更大,植被覆盖增加速度缓慢,达到轻度改善水平;低值区聚集在黄土高原边缘地区,植被增加趋势明显;中部关中平原极少部分地区植被覆盖出现了轻度退化或严重退化,以西安市及临近城市最为典型。（3） 热点区主要分布在秦岭山区及关中平原中部地区,冷点区则集中于黄土高原边缘地区,植被覆盖总体以增长为主。热点区格网数量持续增多,2013年达到最大为45.07%;冷点区域数量不断减少,2017年减少到9.82%;次热点区与次冷点区主要分布在中部平原地带及北部地区,由连片分布转化为零散分布,且总量不断减少。（4） 自然因素对植被覆盖的影响最为突出,其中气温和降水为影响植被覆盖的主导因子,决定力q值分别为0.955和0.931,且气温的影响大于降水的影响;人文因子影响力较为显著,GDP因子决定力q值达到0.387。研究可为当地改善植被覆盖环境提供理论依据。     

Application of Data-Driven Evidential Belief Functions to Prospectivity Mapping for Aquamarine-Bearing Pegmatites,Lundazi District,Zambia

A case application of data-driven estimation of evidential belief functions (EBFs) is demonstrated to prospectivity mapping in Lundazi district (eastern Zambia). Spatial data used to represent recognition criteria of prospectivity for aquamarine-bearing pegmatites include mapped granites, mapped faults/fractures, mapped shear zones, and radioelement concentration ratios derived from gridded airborne radiometric data. Data-driven estimates EBFs take into account not only (a) spatial association between an evidential map layer and target deposits but also (b) spatial relationships between classes of evidences in an evidential map layer. Data-driven estimates of EBFs can indicate which spatial data provide positive or negative evidence of prospectivity. Data-driven estimates of EBFs of only spatial data providing positive evidence of prospectivity were integrated according to Dempster’s rule of combination. Map of integrated degrees of belief was used to delineate zones of relative degress of prospectivity for aquamarine-bearing pegmatites. The predictive map has at least 85% prediction rate and at least 79% success rate of delineating training and validation deposits, respectively. The results illustrate usefulness of data-driven estimation of EBFs in GIS-based predictive mapping of mineral prospectivity. The results also show usefulness of EBFs in managing uncertainties associated with evidential maps.     

Fine-resolution population mapping using OpenStreetMap points-of-interest

Data on population at building level is required for various purposes. However, to protect privacy, government population data is aggregated. Population estimates at finer scales can be obtained through areal interpolation, a process where data from a first spatial unit system is transferred to another system. Areal interpolation can be conducted with ancillary data that guide the redistribution of population. For population estimation at the building level, common ancillary data include three-dimensional data on buildings, obtained through costly processes such as LiDAR. Meanwhile, volunteered geographic information (VGI) is emerging as a new category of data and is already used for purposes related to urban management. The objective of this paper is to present an alternative approach for building level areal interpolation that uses VGI as ancillary data. The proposed method integrates existing interpolation techniques, i.e., multi-class dasymetric mapping and interpolation by surface volume integration; data on building footprints and points-of-interest (POIs) extracted from OpenStreetMap (OSM) are used to refine population estimates at building level. A case study was conducted for the city of Hamburg and the results were compared using different types of POIs. The results suggest that VGI can be used to accurately estimate population distribution, but that further research is needed to understand how POIs can reveal population distribution patterns.