A quantitative framework for analysing long term spatial clustering and vegetation fragmentation in an urban landscape using multi-temporal landsat data

Rapid urbanization threatens urban green spaces and vegetation, demonstrated by a decrease in connectivity and higher levels of fragmentation. Understanding historic spatial and temporal patterns of such fragmentation is important for habitat and biological conservation, ecosystem management and urban planning. Despite their potential value, Local Indicators of Spatial Autocorrelation (LISA) measures have not been sufficiently exploited in monitoring the spatial and temporal variability in clustering and fragmentation of vegetation patterns in urban areas. LISA statistics are an important structural measure that indicates the presence of outliers, zones of similarity (hot spots) and of dissimilarity (cold spots) at proximate locations, hence they could be used to explicitly capture spatial patterns that are clustered, dispersed or random. In this study, we applied landscape metrics, LISA indices to analyse the temporal variability in clustering and fragmentation patterns of vegetation patches in Harare metropolitan city, Zimbabwe using Landsat series data for 1994, 2001 and 2017. Analysis of landscape metrics showed an increase in the fragmentation of vegetation patches between 1994–2017 as shown by the decrease in mean patch size, an increase in number of patches, edge density and shape complexity of vegetation patches. The study further demonstrates the utility of LISA indices in identifying key hot spot and cold spots. Comparatively, the highly vegetated northern parts of the city were characterised by significantly high positive spatial autocorrelation (p < 0.05) of vegetation patches. Conversely, more dispersed vegetation patches were found in the highly and densely urbanized western, eastern and southern parts of the city. This suggest that with increasing vegetation fragmentation, small and isolated vegetation patches do not spatially cluster but are dispersed geographically. The findings of the study underline the potential of LISA measures as a valuable spatially explicit method for the assessment of spatial clustering and fragmentation of urban vegetation patterns.     

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time‐series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log‐linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub‐district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation.     

利用时空立方体挖掘小微地震时空演变模式

挖掘分析小微地震的时空演变模式，可为地震灾害的分析与预报提供辅助决策参考。本文以四川地区的地震监测数据为基础，利用时空立方体融合地震点的空间、时间与属性数据，基于时空热点统计分析方法挖掘小微地震的时空冷热点分布模式。试验结果表明：在试验数据的时域内，四川地区小微地震的热点模式主要表现为连续热点、逐渐减少热点和振荡热点。冷点模式主要是连续冷点，且冷点覆盖范围比热点覆盖范围广。基于时空立方体的时空热点分析方法能够发挥时空统计学的优势，可有效挖掘分析小微地震的时空演变趋势。     

Detecting vehicle traffic patterns in urban environments using taxi trajectory intersection points

Abstract

Detecting and describing movement of vehicles in established transportation infrastructures is an important task. It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the functions of established transportation infrastructures. The detection of traffic patterns consists not only of analyses of arrangement patterns of multiple vehicle trajectories, but also of the inspection of the embedded geographical context. In this paper, we introduce a method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments. Those vehicle trajectory intersection points (TIP) are frequently visited locations within urban road networks and are subsequently formed into density-connected clusters, which are then represented as polygons. For representing temporal variations of the created polygons, we enrich these with vehicle trajectories of other times of the day and additional road network information. In a case study, we test our approach on massive taxi Floating Car Data (FCD) from Shanghai and road network data from the OpenStreetMap (OSM) project. The first test results show strong correlations with periodical traffic events in Shanghai. Based on these results, we reason out the usefulness of polygons representing frequently visited locations for analyses in urban planning and traffic engineering.     

昆明市2000-2010年乡镇人口空间自相关及其时空演变分析

昆明作为云南省的省会城市,其人口的空间分布特征对该地区的城市规划和经济发展具有重要意义.本文以昆明市112个乡镇的矢量数据和2000、2010年的人口数据为基础,运用Moran I指数和Moran I散点图对昆明市2000和2010年的乡镇人口进行了空间自相关及其时空演变分析.结论如下:①2000和2010年,昆明市乡镇人口分布呈现显著的正向空间自相关特征,且人口分布出现以昆明市市中心和滇池为中心的"人口高地带"和北部山区的"人口低地带",这与昆明市海拔"北高南低"和地势"北部起伏大、南部和缓"趋于一致;②地形要素是昆明市乡镇人口2000和2010年空间自相关的基础,而随着交通条件的改善和城市化的发展,2000―2010年10年间昆明市乡镇人口空间自相关显著增强,但人口密度出现较大的空间差异.     

Discovering Spatial Patterns in Origin‐Destination Mobility Data

Mobility and spatial interaction data have become increasingly available due to the wide adoption of location‐aware technologies. Examples of mobility data include human daily activities, vehicle trajectories, and animal movements, among others. In this article we focus on a special type of mobility data, i.e. origin‐destination pairs, and present a new approach to the discovery and understanding of spatio‐temporal patterns in the movements. Specifically, to extract information from complex connections among a large number of point locations, the approach involves two steps: (1) spatial clustering of massive GPS points to recognize potentially meaningful places; and (2) extraction and mapping of the flow measures of clusters to understand the spatial distribution and temporal trends of movements. We present a case study with a large dataset of taxi trajectories in Shenzhen, China to demonstrate and evaluate the methodology. The contribution of the research is two‐fold. First, it presents a new methodology for detecting location patterns and spatial structures embedded in origin‐destination movements. Second, the approach is scalable to large data sets and can summarize massive data to facilitate pattern extraction and understanding.     

基于RS技术的20年来厦门城市热岛格局变化研究

应用IB算法反演厦门市地表温度,整合1996—2013年Landsat-5和Landsat-8卫星影像数据,定量分析厦门市城市热岛的时空变化。结果表明,近20年来厦门市建成区空间分布发生了巨大的变化,热岛的时空演变和建成区的密集度分布基本吻合。     

1952年-2010年中国人口分布与社会经济发展的时空耦合特征分析

人口经济的时空耦合态势是中国区域社会经济发展空间均衡过程的重要体现,可有效地指导中国经济布局、民政建设、交通发展和环境管理等相关政策和措施的制定和实施。在地理信息系统（GIS）的支持下,采用人口分布结构指数、人口密度基尼系数、人口经济重心迁移、空间自相关分析等方法,对1935年-2010年6期县级行政单元人口普查统计数据进行空间插值,实现人口密度分界线的空间刻画及其变化趋势分析,结合1952年-2010年省级行政单元人口、经济统计数据,从人口与经济重心空间耦合及其空间一致性两个角度来揭示社会经济与人口分布的时空差异,剖析人口分布与社会经济的联系。得出以下结论：（1）由于社会历史、自然环境和经济条件的影响,中国人口空间分布的时空演变特征不尽相同,东西部差异较大,且不均衡态势日益明显。（2）胡焕庸线至今仍能很好的概括中国人口东南地狭人稠、西北地广人稀的空间分布格局,但是甘肃、宁夏、内蒙古地区的人口分布已经在局部范围内突破了胡焕庸线的限制,中国人口分布在一定程度上呈现向西部转移的趋势。（3）中国人口分布与经济发展空间一致性的地域分异特征十分明显,但省际一致性在不断缩小,人口集聚具有较强的经济导向性。（4）全国和东部、中部、西部和东北部四大区域在1952年-2010年的人口经济一致性程度及其变化趋势各异：全国人口经济空间一致性程度相对较低,东北地区人口经济一致性程度较高且还在改善,东部与中部保持稳定,西部较差且呈减弱趋势特征。     

基于粗糙集的局部同位模式挖掘算法北大核心CSCD

通过空间同位模式挖掘可发现频繁发生在邻近位置的事件集合,为揭示地理现象间的共生规律提供重要的决策支持。由于空间同位模式存在空间异质性问题,已有方法不能很好地探测出空间同位模式分布的相近性区域。为此,本文从地理属性的相近性方向探测同位模式的分布区域,提出了基于粗糙集的局部空间同位模式挖掘方法。首先,从全局视角提取不频繁的空间同位模式作为候选的局部空间同位模式;然后,对候选同位模式的实例位置进行处理,将其分布的热点区域属性作为粗糙数据集,借助粗糙集探测局部空间同位模式自然的分布区域;最后,度量在这些局部区域的频繁程度,生成所有频繁的局部空间同位模式。通过试验与应用发现,该方法不仅可以探测局部空间同位模式分布的相近性区域,还能反映同位模式分布区域的地理属性信息。     

Length L-function for network-constrained point data

Network-constrained points are constrained by and distributed on road networks, for example, taxi pick-up and drop-off locations. The aggregation pattern (clustering) of network-constrained points (significantly denser than randomly distributed) along roads may indicate spatial anomalies. For example, detecting and quantifying the aggregation with the highest intensity (i.e., the number of taxi pick-up points per network length) can reveal high taxi demand. The network K-function and its derivative (incremental network K-function) have been utilized to identify point aggregations and measure aggregation scale, yet can only identify radius-based planar-scale results, thereby mis-estimating aggregation patterns owing to the network topology configuration heterogeneity. Specifically, complex road networks (e.g., intersections) may incur aggregations despite their low intensity. This study constructs the length L-function for network-constrained points, using its first derivative to quantify the true-to-life aggregation scale and the local function to extract aggregations. Synthetic and practical data experiments show innovative detection of aggregations at the length-based scale and with high intensity, providing a new approach to point pattern analysis of networks unaffected by topological complexity.     

利用轨迹数据提取城市居民出行时空分布特征

介绍了利用出租车轨迹数据提取城市居民出行时空分布特征的过程,包括利用数理统计的方法对出租车上下客事件基于时间进行特征分析;给出了一种融合核密度估计(KDE)与兴趣点(POI)分类的密度聚类算法,实现了出租车上下客热点区域的挖掘以及居民出行活动规律与城市功能区之间关系的发现.?研究表明:居民的出行活动特征在"工-休"日之...     

Identifying insect infestation hot spots: an approach using conditional spatial randomization

Epidemic populations of mountain pine beetle highlight the need to understand landscape scale spatial patterns of infestation. The observed infestation patterns were explored using a randomization procedure conditioned on the probability of forest risk to beetle attack. Four randomization algorithms reflecting different representations of the data and beetle processes were investigated. Local test statistics computed from raster representations of surfaces of kernel density estimates of infestation intensity were used to identify locations where infestation values were significantly higher than expected by chance (hot spots). The investigation of landscape characteristics associated with hot spots suggests factors that may contribute to high observed infestations.     

A time‐geographic approach to quantifying wildlife–road interactions

Recent advances in time geography offer new perspectives for studying animal movements and interactions in an environmental context. In particular, the ability to estimate an animal's spatial location probabilistically at temporal sampling intervals between known fix locations allows researchers to quantify how individuals interact with one another and their environment on finer temporal and spatial scales than previously explored. This article extends methods from time geography, specifically probabilistic space–time prisms, to quantify and summarize animal–road interactions toward understanding related diurnal movement behaviors, including road avoidance. The approach is demonstrated using tracking data for fishers (Martes pennanti) in New York State, where the total probability of interaction with roadways is calculated for individuals over the duration tracked. Additionally, a summarization method visualizing daily interaction probabilities at 60 s intervals is developed to assist in the examination of temporal patterns associated with fishers’ movement behavior with respect to roadways. The results identify spatial and temporal patterns of fisher–roadway interaction by time of day. Overall, the methodologies discussed offer an intuitive means to assess moving object location probabilities in the context of environmental factors. Implications for movement ecology and related conservation planning efforts are also discussed.     

山东省人口空间分布模式及其影响因素分析

基于2010年《山东统计年鉴》和全国第六次人口普查数据,本文以县域为研究单元,通过空间自相关分析人口的空间分布模式,运用因子分析和Pearson相关分析探究人口空间分布模式的影响因素。研究结果表明:山东省人口分布呈现"东北-西南"的空间分布模式,人口空间分布整体上呈现显著的空间集聚,主要集中在青岛市的中心城区;经济增长因素和地形条件是山东省人口空间分布模式形成的主要推动力。     

近80年来中国大陆地区人口密度分界线变化

1935年胡焕庸先生通过对中国人口分布特征的研究,提出了被后人称之为"爱辉-腾冲"线的人口密度分界线。为了研究中国大陆地区近80年来人口分布格局的演变特征,本文基于胡焕庸先生给定的人口比例阈值,以县域边界为基础,依据洛伦兹(Lorenz)曲线原理,精确厘定了1935年—2010年间的7期人口密度分界线,并进一步分析该线的变动过程及其两侧人口比例变化特征。结果显示1935年以后至2010年,人口密度分界线的西北部人口增长速率明显高于东南部,中国人口分布极端不均衡的情况有所缓解。     

Comparing spatiotemporal patterns in Eurasian FPAR derived from two NDVI-based methods

Abstract

A long-term, consistent Fraction of Absorbed Photosynthetically Active Radiation (FPAR) product is necessary to study the spatial and temporal patterns of vegetation dynamics associated with climatic changes and human activities. In this study, Eurasia was selected as the study area. The relationship between FPAR and simple infrared/red ratio relationship (SR FPAR), and that between Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR and a Normalised Difference Vegetation Index (NDVI) look-up table (LUT FPAR) were employed to estimate FPAR from 1982 to 2006 by different land cover types, focusing on the comparisons of spatiotemporal FPAR patterns between the two FPAR datasets. The results showed high agreement between MODIS standard FPAR and estimated FPAR in seasonal dynamics with peak values in July. The LUT FPAR was close to MODIS standard FPAR and larger than SR FPAR. The SR and LUT FPAR showed the same spatial distribution and inter-annual variation patterns and were primarily determined by land cover types. An overall increasing trend in FPAR was observed from 1982 to 2006, with reductions from 1991 to 1994 and 2000 to 2002. The inter-annual dynamics in evergreen broadleaf forests showed a decreasing trend over 25 years, while non-forest vegetation FPAR values had slow, stable growth in inter-annual variation.     

武汉市人口增长与空间分布格局演变

为揭示城镇化背景下武汉市人口时序增长趋势和人口空间分布格局演变特征,本文基于1990年第四次、2000年第五次、2010年第六次全国人口普查和2014年武汉市人口普查数据,运用统计学原理和GIS空间分析方法,分析了武汉市人口增长的时空变化规律;应用人口密度空间分布的数学模型,得到武汉市人口空间分布格局的演变趋势。人口增长研究表明:1990年以后,武汉市人口数量迅速增长,总体上形成了中心聚集、外围扩散的空间发展格局。空间分布研究表明:1)武汉市的人口密度分布更加符合Clark模型,主城区Smeed模型拟合效果更好。近25年来,武汉市城镇化进程迅速,人口向主城区聚集。随着时间发展,主城区逐步呈现初级阶段的郊区化。2)武汉市主城区人口空间分布已经出现多中心化的趋势,并且已经有两个街道发育成为人口次中心。研究结果可为武汉市人口相关决策的制定和城市结构调整提供参考依据,有助于认识我国中部崛起城市的发展规律,为其他城市的发展和规划提供参考和借鉴。     

使用点评数据探测城市商业服务设施的发展规律

大众点评网提供的商业设施及其满意度评价数据为城市商业设施的时空分布与发展规律研究提供了一个重要的信息源,它们来源于分布在道路两侧的商业设施。根据此特征,本文设计了一种基于道路网约束的反映商业服务设施与交通网络关系的密度计算方法,对点评数据中蕴含的设施空间分布、设施数量与其满意度之间关系进行了分析。它将商业设施在空间上的二维分布映射至一维的道路网上,更真实地反映了商业服务设施与所处交通环境的影响,揭示了商业服务设施位置、数量及其满意度之间的关系,为城市规划的定量化研究提供了数值依据。     

Application of remote sensing and geographic information systems (gis) to population studies in the gulf: A case of al ain city (UAE)

This paper is the first ever attempt to study population distribution in Al Ain city in Eastern United Arab Emirates (UAE) through integration of remote sensing and Geographic Information System (GIS). The remote sensing data used in this study included high spatial resolution (1 m) IKONOS imagery of February 17, 2001. For the population related studies IKONOS data offers number of advantages over other satellite images, e.g. it has high spatial resolution, it covers a larger area per image, it cost less per km², and available on a more regular basis. Such characteristics provide a mechanism by which population estimates can be updated with high accuracy and better rate of frequency. The average difference between the population recorded in the 2001 and that estimated from IKONOS images for Al Ain city is found to be equal to 5%. GIS is used for modelling the relationship among population variables and shows result obtained. Empirical model analyses results of this study show that the overall density of the city is consistent with location theories, i.e., declining population density from the Central Business District (CBD). The trend of higher-income people living in peripheries of cities is evident worldwide as it is in Al Ain.