基于城市园林树木景观三维模拟的绿视率估算方法

城市生态景观功能主要是绿色植物对人的视觉作用。绿视率被认为是一种比较好的衡量绿色空间视觉感受的描述因子。目前绿视率的估算主要基于静态的图像或者街景数据,而绿视率是一种动态的量,不同视点有不同的值,且植物是生长变化的。因此,本文提出一种基于数据和模型综合驱动的园林树木三维模拟景观的绿视率计算方法,其利用虚拟地理环境、虚拟植物等技术,通过道路、建筑物等硬质景观数据和树木模型驱动,建立城市园林树木三维景观;根据视觉成像原理,构建虚拟相机,模拟不同视点园林景观的视觉图像,然后识别表征植被信息的像素,从而计算绿视率。本文研发了园林树木景观三维模拟及绿视率估算原型系统,并以城市道路树木景观为例,模拟分析了机动车道中车辆乘客可获得的绿视率,与街景图像提取的绿视率值相近。该方法和系统可用于包含时间变量的不同生长阶段园林树木景观的绿视率评估,支持交互设置不同的视点参数,估算城市任意位置和任意方向的绿视率,评价过去、现在和未来的园林景观绿化质量,以人的视觉感知角度为城市绿地规划提供参考。     

实时路况支持下的南京市消防救援覆盖率时空模式分析

快速城镇化导致我国城市消防应急服务基础设施与城市发展不同步的问题日益凸显,城市消防救援覆盖率评估是提升消防服务质量与优化消防资源配置的重要手段。本文提出一种基于实时路况的城市消防救援覆盖率评估模型,通过考虑消防站管辖区域的空间限制,在2020年9月连续三周时间内利用高德地图API获取消防站到达历史火灾事件的实时出行救援时间,对南京市消防救援覆盖率的时空模式进行分析与挖掘。结果显示：① 南京城市火灾密集区域（简称火灾密集区）消防站的平均出行救援时间约10 min,非火灾密集区约16 min,均比国家规定的5 min到达时间标准明显要长,导致南京市消防站在5 min到达标准下的覆盖率仅为8.2%;② 由于南京火灾密集区消防站的平均行车救援距离仅为非火灾密集区的37%,导致火灾密集区火灾事件等待救援时间明显低于非火灾密集区,尤其火灾密集区西南部、东北部及部分消防站周边火灾事件等待救援时间相对较短,但南京全区火灾事件等待救援时间在5 min以内的比例不足7%,且等待救援时间在5~10 min之间的短距离火灾事件受早晚交通出行高峰期交通拥堵影响最大;③ 南京市消防站救援覆盖率受早晚交通出行高峰影响呈现早晚交通出行高峰期明显低于其他时段的“W”形变化模式,火灾密集区消防站在5 min到达标准下的救援覆盖率从非交通出行高峰期的11.5%降低到交通出行高峰期的8.4%,而非火灾密集区从6.1%降低到5%,火灾密集区东南部石门坎与东山交界区域和北部汉中门与迈皋桥周边区域早晚交通出行高峰时段等待救援时间超过15 min的火灾事件比非交通出行高峰时段明显增多;④ 在5 min到达标准下,南京市消防站救援覆盖率“W”形模式波动最小,10 min到达标准下的平均覆盖率为43.5%且波动最明显,15 min到达标准下的平均覆盖率达到75%。最后根据分析结果给出了南京市消防未来建设发展意见。     

Street networks in relation to landforms: Implications for fast-growing cities简

In addition to socio-economic factors, major landforms may affect the city structure and urban form. Here we show that landforms have significant effects on the city shape and street patterns of the fast-growing Iranian cities of Dezful （a river） and Khorramabad （moun- tains and valleys）, but no clear effects on the cities of Yazd and Nain. Also, where the street orientation is peaked, the Gibbs/Shannon entropy （a measure of dispersion or spread） is low, but increases as the distribution becomes more uniform because of landform constraints. The streets in the old inner parts of all the cities are, on average, shorter and denser （more streets per unit area） than the streets of the newer outer parts. The entropies of the outer parts are also greater than those of the inner parts, implying that the street-length distribution gradually becomes more dispersed or spread as the city expands. All these cities have been fast growing in the past decades, with the newer outer parts expanding rapidly. As shown here, the rapidly formed outer parts （with greater dispersion in street patterns） have significantly different textures from those of the older inner parts, indicating different functionality and growth processes. These quantitative methods for street-network analysis can be used worldwide, particularly for analysing the effects of landforms on city shape and texture.     

Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data

Geographically weighted regression (GWR) is an important local technique for exploring spatial heterogeneity in data relationships. In fitting with Tobler’s first law of geography, each local regression of GWR is estimated with data whose influence decays with distance, distances that are commonly defined as straight line or Euclidean. However, the complexity of our real world ensures that the scope of possible distance metrics is far larger than the traditional Euclidean choice. Thus in this article, the GWR model is investigated by applying it with alternative, non-Euclidean distance (non-ED) metrics. Here we use as a case study, a London house price data set coupled with hedonic independent variables, where GWR models are calibrated with Euclidean distance (ED), road network distance and travel time metrics. The results indicate that GWR calibrated with a non-Euclidean metric can not only improve model fit, but also provide additional and useful insights into the nature of varying relationships within the house price data set.     

Mining candidate causal relationships in movement patterns

In many applications, the environmental context for and drivers of movement patterns are just as important as the patterns themselves. This article adapts standard data mining techniques, combined with a foundational ontology of causation, with the objective of helping domain experts identify candidate causal relationships between movement patterns and their environmental context. In addition to data about movement and its dynamic environmental context, our approach requires as input definitions of the states and events of interest. The technique outputs causal and causal-like relationships of potential interest, along with associated measures of support and confidence. As a validation of our approach, the analysis is applied to real data about fish movement in the Murray River in Australia. The results demonstrate that the technique is capable of identifying statistically significant patterns of movement indicative of causal and causal-like relationships.     

An analysis on movement patterns between zones using smart card data in subway networks

Identifying zones and movement patterns of people is crucial to understanding adjacent regions and the relationship in urban areas. Most previous studies addressed zones or movement patterns separately without analysing simultaneously the two issues. In this article, we propose an integrated approach to discover directly both zones and movement patterns among the zones, referred to as movement patterns between zones (MZPs), from historical boarding behaviours of passengers in subway networks by using an agglomerative clustering method. In addition, evaluation measures of MZPs are suggested in terms of coverage and accuracy. The effectiveness of the proposed approach is finally demonstrated through a real-world data set obtained from smart cards on a subway network in Seoul, Korea.     

Evaluation of Linear and Non-Linear Downscaling Methods in Terms of Daily Variability and Climate Indices: Surface Temperature in Southern Ontario and Quebec,Canada

We downscaled atmospheric reanalysis data using linear regression and Bayesian neural network (BNN) ensembles to obtain daily maximum and minimum temperatures at ten weather stations in southern Quebec and Ontario, Canada. Performance of the linear and non-linear downscaling models was evaluated using four different sets of predictors, not only in terms of their ability to reproduce the magnitude of day-to-day variability (i.e., “weather,” mean absolute error between the daily values of the predictand(s) and the downscaled data) but also in terms of their ability to reproduce longer time scale variability (i.e., “climate,” indices of agreement between the predictand's observed annual climate indices and the corresponding downscaled values). The climate indices used were the 90th percentile of the daily maximum temperature, 10th percentile of the daily minimum temperature, number of frost days, heat wave duration, growing season length, and intra-annual temperature range.

Our results show that the non-linear models usually outperform their linear counterparts in the magnitude of daily variability and, to a greater extent, in annual climate variability. In particular, the best model simulating weather and climate was a BNN ensemble using stepwise selection from 20 reanalysis predictors, followed by a BNN ensemble using the three leading principal components from the aforementioned predictors. Finally, we showed that, on average, the first three indices presented higher skills than the growing season length, number of frost days, and the heat wave duration.     

突发灾害监控与决策系统的设计与实现

现代社会,面对高危或人类无法到达的区域,如森林火场现场视查、地震灾情查看、城市等狭窄地段的高楼消防救灾、核辐射现场勘察检测等,基于无人机的无线通信系统,由于体积小和机动灵活,通过信息传感采集、视频现场捕捉,能安全、有效地获取现场灾情情况信息。然后数据通过中继传到后台,存入后台数据库。利用采集到的现场信息与Arc GIS Server发布的GIS相关服务,通过Arc GIS API for Flex对数据进行快速分析处理,使采集信息可视化,同时进行突发灾害模拟预测,准确定位事发地点,评价灾害影响范围,查找救援最短路径等。本文设计并实现了基于无人机突发灾害监控与决策系统,同时重点介绍系统的设计、组网的关键技术、视频压缩、基于GIS的数据处理与分析以及系统实现的功能。     

基于双阈值SSDA模板匹配的遥感图像道路边缘检测研究

利用遥感图像提取道路边缘信息可以简化常规城市道路的测绘工作。根据一般道路在图像上的影像特征对图像进行模型化处理,提出了基于双阈值的序贯相似性检测算法(sequential similarity detection algorithm,SSDA)的模板匹配算法,并在该算法的基础上,提出了一个基于减小增长误差的算子,用以减少由于样本数过多而产生的增长误差。与其他算法相比,该算法能够更有效地获取道路边缘信息,在准确提取路面边缘信息的同时,能对预处理中难以完全提取的路面部分进行修复,减少处理过程,从而提高处理效率。     

北京交通网络拓扑结构及可达性格局历史变化研究

基于过去800年（1160～2000年）北京城市交通网络时间序列数据,研究了北京交通网络的发展变化。主要从交通网络的拓扑结构、可达性等角度分别对网络直径、平均最短路径长度、平均聚集指数、相对可达性等指标进行了定性定量分析。研究结果表明：第一,北京交通网络扩张呈“两段式”特点：前数百年只加密路网,近53年网络直径扩张了2．62倍;第二,从网络拓扑结构的角度分析,北京过去800年交通网络度分布结构呈现良好的一致性,交通网络中顶点与路段的比例保持不变,网络内部联系日趋紧密;第三,北京交通网络可达性格局趋于有序：从金元时期的不规则格局发展为明清时期的椭圆形格局,建国后呈圆盘形格局扩张,但可达性中心由天安门向西北方向偏移至阜成门处。通过搜集研究北京过去800年的交通网络数据,本文记录了北京交通网络演化的全过程,并揭示了其演化过程中蕴含的宏观规律。