Do suburban residents prefer the fastest or low-carbon travel modes? Combining public participation GIS and multimodal travel time analysis for daily mobility research

Sustainability of urban transportation and residents' daily mobility is increasingly important for the future of urban regions. In this paper, we present a novel methodology combining mapped survey responses (gathered using public participation GIS) and sophisticated multimodal routing analysis to understand patterns of suburban residents' daily mobility. Our particular focus is on residents' mode choices and their optimality in terms of travel time. We also examine the carbon-intensity of potential mode choice mismatches where a comparatively slower travel mode is chosen for a particular trip. This paper shows how the combination of the two data sources create new possibilities for analysing daily mobility by enabling the comparison of self-reported, geographically referenced mobility patterns and modelled, objectively “optimal” patterns. Our case study results from a rapidly growing metropolitan area, Greater Helsinki in Finland, indicate that people have rather sustainable daily mobility practices. Their daily trips are typically short (median 1.8 km), and non-motorised travel modes are the most popular. Also the share of car travel is significant. While public transport clearly has the lowest modal share, it is frequently used on longer trips to destinations where public transport connections are good (particularly the city centre). In 44% of trips, the respondents choose the fastest travel mode. When a comparatively slower travel mode is chosen, it most often produces less CO₂ emissions than the fastest mode. However, particularly on shopping trips – the most commonly reported type of trip – car driving dominates as the fastest means of transportation. When a slower mode is chosen on a shopping trip, it is typically more carbon-intensive than the fastest mode would have been in that case. The methodology presented here can be used to support local decision making, particularly when daily services and transport infrastructure are planned or reorganised.     

Large-scale land cover mapping with the integration of multi-source information based on the Dempster–Shafer theory

Land cover type is a crucial parameter that is required for various land surface models that simulate water and carbon cycles, ecosystem dynamics, and climate change. Many land use/land cover maps used in recent years have been derived from field investigations and remote-sensing observations. However, no land cover map that is derived from a single source (such as satellite observation) properly meets the needs of land surface simulation in China. This article presents a decision-fuse method to produce a higher-accuracy land cover map by combining multi-source local data based on the Dempster–Shafer (D–S) evidence theory. A practical evidence generation scheme was used to integrate multi-source land cover classification information. The basic probability values of the input data were obtained from literature reviews and expert knowledge. A Multi-source Integrated Chinese Land Cover (MICLCover) map was generated by combining multi-source land cover/land use classification maps including a 1:1,000,000 vegetation map, a 1:100,000 land use map for the year 2000, a 1:1,000,000 swamp-wetland map, a glacier map, and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001). The merit of this new map is that it uses a common classification system (the International Geosphere-Biosphere Programme (IGBP) land cover classification system), and it has a unified 1 km resolution. The accuracy of the new map was validated by a hybrid procedure. The validation results show great improvement in accuracy for the MICLCover map. The local-scale visual comparison validations for three regions show that the MICLCover map provides more spatial details on land cover at the local scale compared with other popular land cover products. The improvement in accuracy is true for all classes but particularly for cropland, urban, glacier, wetland, and water body classes. Validation by comparison with the China Forestry Scientific Data Center (CFSDC)–Forest Inventory Data (FID) data shows that overall forest accuracies in five provinces increased to between 42.19% and 88.65% for our MICLCover map, while those of the MODIS2001 map increased between 27.77% and 77.89%. The validation all over China shows that the overall accuracy of the MICLCover map is 71%, which is higher than the accuracies of other land cover maps. This map therefore can be used as an important input for land surface models of China. It has the potential to improve the modeling accuracy of land surface processes as well as to support other aspects of scientific land surface investigations in China.     

Classification of full-polarization ALOS-PALSAR imagery using SVM in arid area of Dunhuang

Classification is an important process in interpretation of synthetic aperture radar(SAR) imagery. As an advanced instrument for remote sensing, the polarimetric SAR has been applied widely in many fields. The main aim of this paper is to explore the ability of the full-polarization SAR data in classification. The study area is a part of Dunhuang, Gansu Province, China. An L-band full-polarization image of Dunhuang which includes quad-polarization modes was acquired by the ALOS-PALSAR(Advanced Land Observing Satellite–the Phased Array type L-band Synthetic Aperture Radar). Firstly, new characteristic information was extracted by the difference operation, ratio operation, and principal component transform based on the full-polarization(HH, HV or VH, VV) modes SAR data. Then the single-, dual-, full-polarization SAR data and new SAR characteristic information were used to analyze quantitatively the classification accuracy based on the Support Vector Machines(SVM). The results show that classification overall accuracy of single-polarization SAR data is poor, and the highest is 56.53% of VV polarization. The classification overall accuracy of dual-polarization SAR is much better than single-polarization, the highest is 74.77% of HV VV polarization data. The classification overall accuracy of full-polarization SAR is 80.21%, adding the difference characteristic information, ratio characteristic information and the first principal component(PC1) respectively, the overall accuracy increased by 3.09%, 3.38%, 4.14% respectively. When the full-polarization SAR data in combination with the all characteristic information, the classification overall accuracy reached to 91.01%. The full-polarization SAR data in combination with the band math characteristic information or the PC1 can greatly improve classification accuracy.     

城市物流园区类型及运营模式——以深圳市为例

物流园区作为城市物流活动的重要空间载体,其发展对城市物流产业的发展具有重要意义。但对物流园区相关研究主要以园区未来规划为主,缺少对发展成熟物流园区的案例研究。鉴于此,文章以物流园区发展较早的深圳市9大物流园区为案例,将其划分为货运枢纽型、城市配送型和综合功能型物流园区3种类型,并总结其在选址、用地规模、业务类型方面的特征;根据政府参与管理程度,将其划分为政府主导型、企业主导型和半政府主导型3种运营模式,并认为城市物流园区运营模式与园区类型具有密切关系。文章从物流园区的依附设施、用地规模、投资规模3方面分析了其对运营模式的影响。     

基于QUEST决策树的遥感影像土地利用分类——以云南省丽江市为例

土地利用分类精度直接决定土地利用/土地覆被变化相关研究的准确性,而基于决策树的遥感影像分类是近年来提高土地利用分类精度的重要方法。QUEST决策树在影像解译和空间表达方面,运算速度和分类精度均优于普通CART等决策树方法。本文以云南丽江地区为例,应用QUEST决策树分类方法,对该地区的Landsat TM 5影像图进行分类,同时将地形因素、植被指数作为地学辅助数据的因子添加到分类波段中,进行不同特征融合,来处理目标类别间的非线性关系,该方法在处理图像理解知识方面具有更大的灵活性;同时与普通决策树分类法的遥感影像分类的结果相比较,Kappa系数值从原来的0.789提高到0.849.在地形复杂的山地地区,针对TM影像数据,选择基于QUEST决策树分类能够有效提高土地利用分类结果精度。     

Why GPS makes distances bigger than they are

Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS; the distance between two points recorded with a GPS is – on average – bigger than the true distance between these points. This systematic ‘overestimation of distance’ becomes relevant if the influence of interpolation error can be neglected, which in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and illustrate that it functionally depends on the autocorrelation of GPS measurement error (C). We argue that C can be interpreted as a quality measure for movement data recorded with a GPS. If there is a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine C in real-world GPS movement data sampled at high frequencies. We apply our approach to pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.     

基于出行链的建成环境对居民小汽车通勤出行的影响

城市建成环境对居民出行,尤其是小汽车通勤出行的影响是交通需求预测的基础性命题,长期受到关注.已有研究大多以单次出行作为研究对象,较少关注出行链影响下的出行协同决策问题.论文基于2017年广州市居民活动日志调查数据,采用Logistic模型检验了出行链视角下城市建成环境对工作日选择小汽车通勤出行的影响.研究发现,出行链中...     

基于大数据的上海市共享汽车出行模式研究

共享汽车是城市交通发展的新潮流,论文基于2018年上海市共享汽车运营大数据和城市POI数据,研究共享汽车用户出行的空间、时间和频次特征,并对用户开展出行模式的K均值聚类分析,以期挖掘典型出行规律,为共享汽车运营优化提供依据。研究表明：① 工作日与休息日共享汽车使用行为差异显著,工作日共享汽车出行较集中于中心城区的混合功能区,早晚通勤高峰使用量大;休息日共享汽车出行空间布局较分散,使用量更高、平均单次使用时长更短且仅存在傍晚一个使用高峰。② 上海市共享汽车出行行为可分为工作日通勤的中高频模式,工作日夜间活动的高频模式,工作日偶尔用车晚餐及返家、偶尔用车远距离通勤和夜间长距离返家的低频模式,休息日日间休闲的高频模式、休息日傍晚离家休闲的中长途低频模式、休息日傍晚在休闲地之间穿梭的远距离低频模式、休息日日间加班的低频模式等10种模式。③ 中高频用户主要使用共享汽车实现远距离通勤和周末中长距离休闲活动,空间区域主要集中在中心城区和副中心区域;低频用户使用共享汽车多为夜间、长距离、休息日加班等公共交通难以满足需求、出租车成本过高的情景,空间分布也比较分散。可通过对用户提供差别化用车方案、优化车辆空间调度等方式推进超大城市共享汽车市场发展。     

旅游地快速交通优势度与旅游流强度的空间耦合分析

以典型旅游地—云南省为研究案例,以高德交通大数据、统计数据等多源数据为基础,依据“路网及站点密度+通行规模+通行功能+区位优势度+换乘便捷度”的思路,构建快速交通优势度模型;基于旅游流“规模→消费→效益→效应”的历时性维度构建旅游流强度模型;采用加权TOPSIS法对二者评价值进行测算,并运用耦合四象限模型对两者耦合类型进行划分。结果发现：① 快速交通与旅游流耦合存在显著空间差异性。昆明、红河和丽江呈现良性耦合协调,耦合类型表现为“高旅游流-高快速交通优势”,而旅游化水平低、远离交通枢纽和主要交通干线的边缘地区,旅游流与快速交通耦合效应则表现为“低旅游流-低快速交通优势”。② 快速交通优势度与旅游流强度呈正相关关系,不同快速交通方式与旅游流强度的拟合优度表现为“航空运输>高速公路>高速铁路”的特征。③ 云南省快速交通优势度与旅游流强度耦合水平总体偏低,快速交通发展的主导模式为协调互补模式,且缘于快速交通的“时间-空间收敛”效应和“组织-空间协同”效应,快速交通组合类型多样化与旅游流强度存在正相关关系。不同快速交通发展模式对旅游流强度的贡献效应表现出“多元共生模式>协调互补模式>单类孤立模式>低速交通维持模式”的特征。     

邻里环境感知与个体健康对通勤模式选择的影响研究——以南京市为例

通勤是大部分城市就业者日常工作所必须面对的问题。相比宏观层面的城市建成环境,微观层面的邻里环境感知及个体健康与通勤模式选择的联系更为密切。以南京市主城区8个社区为案例地,利用结构方程模型探究邻里环境感知与个体健康对城市就业者通勤模式选择的影响。首先借助问卷调查数据,将通勤模式分为积极通勤、公共交通通勤、电动车/摩托车通勤以及小汽车通勤四类;其次使用因子分析法确定邻里环境感知的潜变量,并将个体健康分为身体健康、心理健康与健康行为3个潜变量;最后构建结构方程模型的理论框架并进行实证分析。研究发现：① 邻里环境感知变量中服务设施感知、环境品质感知对通勤模式选择存在显著的直接效应,交通安全感知对通勤模式选择存在显著的间接效应。② 个体健康变量中,心理健康与健康行为对通勤模式选择存在显著的直接效应。③ 通勤距离与社会经济属性对通勤模式选择存在显著的直接效应,同时社会经济属性对通勤模式选择亦存在显著的间接效应。基于研究结果,提出有效提升就业者对于社区环境的主观感受,进而提高就业者参与健康行为的意愿与就业者的健康水平,从而引导其选择更加健康和绿色的通勤模式。     

An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification∗

Object-oriented image classification has tremendous potential to improve classification accuracies of land use and land cover (LULC), yet its benefits have only been minimally tested in peer-reviewed studies. We aim to quantify the benefits of an object-oriented method over a traditional pixel-based method for the mixed urban–suburban–agricultural landscape surrounding Gettysburg, Pennsylvania. To do so, we compared a traditional pixel-based classification using maximum likelihood to the object-oriented image classification paradigm embedded in eCognition Professional 4.0 software. This object-oriented paradigm has at least four components not typically used in pixel-based classification: (1) the segmentation procedure, (2) nearest neighbor classifier, (3) the integration of expert knowledge, and (4) feature space optimization. We evaluated each of these components individually to determine the source of any improvement in classification accuracy. We found that the combination of segmentation into image objects, the nearest neighbor classifier, and integration of expert knowledge yields substantially improved classification accuracy for the scene compared to a traditional pixel-based method. However, with the exception of feature space optimization, little or no improvement in classification accuracy is achieved by each of these strategies individually.     

基于腾讯迁徙大数据的中国城市网络研究

随着经济全球化和区域一体化的快速发展,城市间的交流日益密切,要素的流动性和互赖性促进了城市网络的形成,并成为一种新的区域组织模式和空间结构.本文基于2018年腾讯人口迁徙数据,构建了372×372关系数据矩阵,并从不同交通方式出发,系统刻画了中国城市网络格局.研究表明:①网络关联度高的城市主要集中在胡焕庸线以东区域,尤...     

基于多类型无人机数据的红树林遥感分类对比

使用固定翼无人机、消费级旋翼无人机和专业级旋翼无人机获取广东珠海淇澳岛红树林保护区多类型无人机遥感影像,使用基于面向对象分类的K-最近邻与随机森林分类器对研究区影像进行红树林树种精细分类和对比分析,并探讨了不同类型无人机平台在红树林资源调查应用中的优缺点。结果表明：1）固定翼无人机、消费级旋翼无人机和专业级旋翼无人机数据使用K-最近邻法的分类精度分别为：73.8%、72.8%和79.7%;使用随机森林法的分类精度分别为：81.1%、84.8%和89.3%。3种平台类型的无人机数据均适用于红树林精细分类研究,对于无人机红树林遥感数据,随机森林的分类方法优于K-最近邻方法。2）以拍摄面积与用时之比估算采集效率,固定翼无人机、消费级旋翼无人机和专业级旋翼无人机分别为0.036、0.013和0.003 km2/min。固定翼无人机的采集效率具有明显优势。3）固定翼无人机适合大范围红树林数据采集,要求较高;消费级旋翼无人机适于获取小范围精细数据,成本低且易学易用;专业级旋翼无人机适合搭载质量稍大的如成像光谱仪、LiDAR等专业传感器获取多源数据。最后给出了无人机在红树林遥感研究中的注意事项和建议。     

遥感影像分类与地学知识发现的集成研究

遥感与地学之间存在着数据与知识上巨大的互补性。本文通过地面类型数据将遥感影像分类与地学知识发现结合起来:用遥感数据驱动发现地学知识,用地学知识解释、确认、检验遥感分类结果,并使用统计值和分布谱来定量化表达地学知识,形成一体化的遥感地学分类系统。     

地学应用中的遥感图像处理若干问题的分析

遥感技术在地理学应用中是如何从遥感影像上直观、准确的得到所需的信息,为本专业服务。文章从地学应用部门在进行遥感影像处理时遇到的几个问题:多光谱数据的选取与合成;多源信息的复合;新型图像分类器的应用;专题提取的精度等方面的进展作了分析。     

城市行政中心区位选择与迁移类型 ——以深莞惠都市区为例

行政中心迁移作为重要行政决策,对城市发展战略和近期建设重心有显著影响作用。本文采用层次演进分析法,对珠三角东岸中心城市深圳、东莞和惠州进行行政中心的分布规律与影响要素实证研究。研究结果表明：我国行政中心概念出现演化,形成服务中心与配套设施逐步分离的需求;行政中心迁移可分为区域一体化发展、空间管治的驱动和新增长极的创造等类型。对珠三角深莞惠都市区城市行政中心迁移模式进行研究,结果显示：深圳选择行政中心跨区迁移,其迁移核心体现在与珠三角合作的区域一体化和深港同城化2个层面;东莞行政中心迁移源于空间管治驱动,以达成新旧中心城市功能的置换;惠州行政中心跨江迁移,为的是促进新城市增长极的形成。3种迁移模式的成功实现有助于探索都市区行政中心与经济中心的耦合互动发展路径,对我国都市区发展有借鉴和启示作用。     

Knowledge discovery from soil maps using inductive learning

This paper develops a knowledge discovery procedure for extracting knowledge of soil-landscape models from a soil map. It has broad relevance to knowledge discovery from other natural resource maps. The procedure consists of four major steps: data preparation, data preprocessing, pattern extraction, and knowledge consolidation. In order to recover true expert knowledge from the error-prone soil maps, our study pays specific attention to the reduction of representation noise in soil maps. The data preprocessing step has exhibited an important role in obtaining greater accuracy. A specific method for sampling pixels based on modes of environmental histograms has proven to be effective in terms of reducing noise and constructing representative sample sets. Three inductive learning algorithms, the See5 decision tree algorithm, Naïve Bayes, and artificial neural network, are investigated for a comparison concerning learning accuracy and result comprehensibility. See5 proves to be an accurate method and produces the most comprehensible results, which are consistent with the rules (expert knowledge) used in producing the soil map. The incorporation of spatial information into the knowledge discovery process is found not only to improve the accuracy of the extracted knowledge, but also to add to the explicitness and extensiveness of the extracted soil-landscape model.     

Measures of transport mode segmentation of trajectories

ABSTRACT

Rooted in the philosophy of point- and segment-based approaches for transportation mode segmentation of trajectories, the measures that researchers have adopted to evaluate the quality of the results (1) are incomparable across approaches, hence slowing the progress in the field and (2) do not provide insight about the quality of the continuous transportation mode segmentation. To address these problems, this paper proposes new error measures that can be applied to measure how well a continuous transportation mode segmentation model performs. The error measures introduced are based on aligning multiple inferred continuous intervals to ground truth intervals, and measure the cardinality of the alignment and the spatial and temporal discrepancy between the corresponding aligned segments. The utility of this new way of computing errors is shown by evaluating the segmentation of three generic transportation mode segmentation approaches (implicit, explicit–holistic, and explicit–consensus-based transport mode segmentation), which can be implemented in a thick client architecture. Empirical evaluations on a large real-word data set reveal the superiority of explicit–consensus-based transport mode segmentation, which can be attributed to the explicit modeling of segments and transitions, which allows for a meaningful decomposition of the complex learning task.     

Land cover classification by an artificial neural network with ancillary information

Abstract

Remote sensing is an important source of land cover data required by many GIS users. Land cover data are typically derived from remotely–sensed data through the application of a conventional statistical classification. Such classification techniques are not, however, always appropriate, particularly as they may make untenable assumptions about the data and their output is hard, comprising only the code of the most likely class of membership. Whilst some deviation from the assumptions may be tolerated and a fuzzy output may be derived, making more information on class membership properties available, alternative classification procedures are sometimes required. Artificial neural networks are an attractive alternative to the statistical classifiers and here one is used to derive a fuzzy classification output from a remotely–sensed data set that may be post–processed with ancillary data available in a GIS to increase the accuracy with which land cover may be mapped. With the aid ancillary information on soil type and prior knowledge of class occurrence the accuracy of an artificial neural network classification was increased by 29–93 to 77–37 per cent. An artificial neural network can therefore be used generate a fuzzy classification output that may be used with other data sets in a GIS, which may not have been available to the producer of the classification, to increase the accuracy with which land cover may be classified.     

“交旅”融合下旅游效率与高速交通协调格局研究——以长三角41市为例

长三角是众多旅游要素的集聚地,高速交通的要素集聚与扩散效应对城市旅游效率增长至关重要.以长三角41市为研究对象,基于多源异构数据构建高速交通与旅游效率评价指标体系,在采用VAR模型验证旅游效率与高速交通关系,以及构建二者耦合机理基础上,综合采用TOPSIS方法、Bootstrap-DEA模型和耦合协调模型对2005-2...