空间数据分析一流课程建设的思考

空间分析/空间数据分析是GIS的核心与灵魂。GIS已从数据库型GIS进入分析型GIS阶段。《空间分析/空间数据分析》已逐步成为遥感科学与技术、地理信息科学、地理空间信息工程等本科专业的核心课程。课程是人才培养的核心要素、课程质量直接决定人才培养质量、课程建设永远在路上。围绕空间数据分析一流课程建设,重点讨论了课程名称、课程内容体系、教育教学方法、教学资源共享等问题。经讨论提出了5条建议:①将课程名称统一改为《空间数据分析》;②重塑课程内容体系,在介绍基本空间分析方法的基础上,加大空间统计分析部分的比重,并适当补充轨迹分析、智能空间分析等内容;③面向学生综合能力培养,进一步改革教学方式,逐步推动学生从被动学习到主动学习的转变,并以产出为导向改革课程评定和评价方法;④不同学校根据各自的特点,分类型、分层次地开展《空间数据分析》课程的教学,编写不同类型和层次的空间数据分析教材;⑤建立《空间数据分析》课程的教学联盟和虚拟教研室,发挥各高校师资和教学资源的综合力量,实现教学资源共享、教学经验及时交流等,为培养空间数据分析优秀人才提供重要基础。

空间分析与建模研究生课程教学模式改革探讨

针对GIS专业空间分析系列课程教学存在的问题,从"问题解决"与"科研课题"双重驱动的角度对《空间分析与建模》研究生课程进行教学改革的探索与实践。将教学重点放在实践技能训练与学术思维培养,从课程组织结构、知识体系、教学方式方法、考核制度等多方面进行改革实践,形成一整套教学改革方案,取得了较好的改革成效,为空间分析系列课程提供了新的教学思路。

城市群轨道交通网络化规划评价研究

研究目的:城市群轨道交通网络化规划评价是规划方案选择的主要依据,本文构建科学客观且具有普适性的评价指标体系及评价方法,为城市群轨道交通发展提供理论指导。通过建立包含线网结构、运营效果、社会效益、城镇协调、实施难度5项一级指标,23个二级指标的城市群轨道交通网络化规划评价指标体系,采用灰色关联度法和信息熵权法组合赋权确定指标权重,构建灰色模糊多层次评价模型,实现对总目标、一级指标、二级指标的综合评价分析。研究结论:(1)案例分析表明本文提出的规划评价体系及方法科学可行;(2)城市群轨道交通网络化规划评价准则层影响程度从大到小依次为线网结构、城镇协调、运营效果、社会效益和实施难度;(3)本研究成果适用于城市群轨道交通网络化规划方案优劣评价,可为城市群轨道交通规划建设提供决策参考。

面向中小学生的“智教通”手机APP的设计与实现

当今社会诱惑较多,使得青少年教育问题成为了所有有孩子家庭的重大难题。笔者针对中小学生的教育问题,设计了包括iOS、Android和Web版本的"智教通"信息交流平台。本平台的实现方便了学生家长及时了解学生动态,方便了老师与家长的信息交流。笔者介绍了系统的设计、主要功能与实现,为教师和家长提供一个面向中小学生的便捷管理平台。

基于腾讯位置数据的南昌市活动空间凝聚结构与区划研究

近年来地理信息大数据运用在人文地理学领域成为了新热潮。本文借助腾讯位置数据,得到了工作日、休息日、节假日三种类型的数据,以南昌市为例,从居民实际活动的视角出发,首先对研究区域的活动空间时序变化特征进行研究,在此基础上,通过核密度分析法总结了研究区域的活动空间凝聚结构,最后利用热度山顶点提取和泰森多边形等方法对具体的城市活动空间功能区进行划分与分析。研究发现:①工作日的高频活动区域面积明显高于休息日和节假日,分类区间的热力值越大像元数量越小。②研究区域城市活动空间的凝聚结构有四种,分别为"单核结构"、"双核结构"、"三核结构"、"集聚结构",每种结构都有独特的形态特征以及代表性的典型区域。③研究区域可划分为六种活动空间功能区划:综合活动区、居住活动区、学习活动区、休闲娱乐区、交通枢纽区、就业活动区,不同的活动模式对应着各自的活动频度变化规律,且运用叠加分析法识别出六种功能模式的核心区和边缘区。④研究区域的活动功能区分布不均,大部分的活动类型为居住活动区和学习活动区,但主城区的居民活动类型更加丰富,主城区的集聚程度高于远城区,且活动功能区与空间凝聚结构具有一定联系。⑤根据大数据挖掘发现的南昌市活动空间凝聚结构分布特征、活动功能区的分布特征,结合南昌市城市发展规划,对南昌市目前的活动空间结构进行评价,从而对昌南、昌北、城东的发展提出相应建议。

Exploring the epidemic transmission network of SARS in-out flow in China's Mainland

The changing spatiotemporal patterns of the individual susceptible-infected-symptomatic-treated-recovered epidemic process and the interactions of information/material flows between regions,along with the 2002-2003 Severe Acute Respiratory Syndrome(SARS) epidemiological investigation data in China's Mainland,including three typical locations of individuals(working unit/home address,onset location and reporting unit),are used to define the in-out flow of the SARS epidemic spread.Moreover,the input/output transmission networks of the SARS epidemic are built according to the definition of in-out flow.The spatiotemporal distribution of the SARS in-out flow,spatial distribution and temporal change of node characteristic parameters,and the structural characteristics of the SARS transmission networks are comprehensively and systematically explored.The results show that(1) Beijing and Guangdong had the highest risk of self-spread and output cases,and prevention/control measures directed toward self-spread cases in Beijing should have focused on the later period of the SARS epidemic;(2) the SARS transmission networks in China's Mainland had significant clustering characteristics,with two clustering areas of output cases centered in Beijing and Guangdong;(3) Guangdong was the original source of the SARS epidemic,and while the infected cases of most other provinces occurred mainly during the early period,there was no significant spread to the surrounding provinces;in contrast,although the input/output interactions between Beijing and the other provinces countrywide began during the mid-late epidemic period,SARS in Beijing showed a significant capacity for spatial spreading;(4) Guangdong had a significant range of spatial spreading throughout the entire epidemic period,while Beijing and its surrounding provinces formed a separate,significant range of high-risk spreading during the mid-late period;especially in late period,the influence range of Beijing's neighboring provinces,such as Hebei,was even slightly larger than that of Beijing;and(5) the input network had a low-intensity spread capacity and middle-level influence range,while the output network had an extensive high-intensity spread capacity and influence range that covered almost the entire country,and this spread and influence indicated that significant clustering characteristics increased gradually.This analysis of the epidemic in-out flow and its corresponding transmission network helps reveal the potential spatiotemporal characteristics and evolvement mechanism of the SARS epidemic and provides more effective theoretical support for prevention and control measures.

Spatial-temporal characteristics of epidemic spread in-out flow——Using SARS epidemic in Beijing as a case study

For better detecting the spatial-temporal change mode of individual susceptible-infected-symptomatic-treated-recovered epidemic progress and the characteristics of information/material flow in the epidemic spread network between regions,the epidemic spread mechanism of virus input and output was explored based on individuals and spatial regions.Three typical spatial information parameters including working unit/address,onset location and reporting unit were selected and SARS epidemic spread in-out flow in Beijing was defined based on the SARS epidemiological investigation data in China from 2002 to 2003 while its epidemiological characteristics were discussed.Furthermore,by the methods of spatial-temporal statistical analysis and network characteristic analysis,spatial-temporal high-risk hotspots and network structure characteristics of Beijing outer in-out flow were explored,and spatial autocorrelation/heterogeneity,spatial-temporal evolutive rules and structure characteristics of the spread network of Beijing inner in-out flow were comprehensively analyzed.The results show that(1)The outer input flow of SARS epidemic in Beijing concentrated on Shanxi and Guangdong provinces,but the outer output flow was disperse and mainly includes several north provinces such as Guangdong and Shandong.And the control measurement should focus on the early and interim progress of SARS breakout.(2)The inner output cases had significant positive autocorrelative characteristics in the whole studied region,and the high-risk population was young and middle-aged people with ages from 20 to 60 and occupations of medicine and civilian labourer.(3)The downtown districts were main high-risk hotspots of SARS epidemic in Beijing,the northwest suburban districts/counties were secondary high-risk hotspots,and northeast suburban areas were relatively safe.(4)The district/county nodes in inner spread network showed small-world characteristics and information/material flow had notable heterogeneity.The suburban Tongzhou and Changping districts were the underlying high-risk regions,and several suburban districts such as Shunyi and Huairou were the relatively low-risk safe regions as they carried out minority information/material flow.The exploration and analysis based on epidemic spread in-out flow help better detect and discover the potential spatial-temporal evolutive rules and characteristics of SARS epidemic,and provide a more effective theoretical basis for emergency/control measurements and decision-making.