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。     

GIS-based spatiotemporal analysis of forest fires in Turkey from 2010 to 2020

Forests are essential in contributing to the continuity of the natural balance. Therefore, their protection and sustainability are vital. However, all over the world, forest fires occur, and forests are destroyed due to both human factors and unknown causes. It is necessary to carry out studies to prevent this destruction. At this point, GIS-based location–time relationship-based hot spot clustering analysis can provide significant advantages in detecting risky spots of forest fires. In this study, GIS-based emerging hot spot clustering analysis was carried out to determine the risky areas where forest fires will occur and to carry out preventive studies in the relevant areas. Turkey was chosen as the pilot region, and analyses were carried out using the data obtained from the official statistics of the Ministry of Agriculture and Forestry General Directorate of Forestry according to the causes of the fires (negligence, intentional, accidental, unknown cause and natural) between the years 2010 and 2020. Spatial autocorrelation analysis was conducted for each fire type, and threshold distances were determined {with a number of distance bands = 20,000, distant increment = 10,000}. Emerging hot spot analyses were then conducted, and the results were presented as maps and statistical outputs. According to all fire types, 15 new hot spots, 14 persistent hot spots, 33 sporadic hot spots, 9 consecutive hot spots, 15 intensifying, and 2 diminishing hot spot regions were obtained throughout the country.     

Spatiotemporal analysis of photo contribution patterns to Panoramio and Flickr

ABSTRACT

Photo-sharing services provide a rich resource of crowdsourced spatial data consisting of georeferenced imagery and metadata. Shared photos can provide valuable information for a variety of applications and geospatial analysis tasks, such as identifying tourist hot spots or traveled routes. Understanding the spatiotemporal patterns of photo contributions will allow analysts to assess the suitability of these data for related analysis tasks. Using California as a study area, this paper analyzes various aspects of photo contribution patterns of Panoramio and Flickr. It identifies areas where annual photo contributions are still growing and areas that undergo a decline in annual contributions. Multiple regression is used to identify which environmental correlates are associated with an increase in photo-sharing activities. Furthermore, panel data of annual contributions between 2006 and 2013 for California subcounties will be used in a regression model to demonstrate that there is a positive feedback effect between Panoramio and Flickr photo contributions, but no neighborhood effect. The results of this paper provide insight into the data quality of crowdsourced image collections. These collections are commonly used for geospatial applications, including tourist information services and the computation of scenic routes.     

Predictive fire occurrence modelling to improve burned area estimation at a regional scale: A case study in East Caprivi,Namibia

Fires threaten human lives, property and natural resources in Southern African savannas. Due to warming climate, fire occurrence may increase and fires become more intense. It is crucial, therefore, to understand the complexity of spatiotemporal and probabilistic characteristics of fires. This study scrutinizes spatiotemporal characteristics of fires and the role played by abiotic, biotic and anthropogenic factors for fire probability modelling in a semiarid Southern African savanna environment. The MODIS fire products: fire hot spots (MOD14A2 and MYD14A2) and burned area product MODIS (MCD45A1), and GIS derived data were used in analysis. Fire hot spots occurrence was first analysed, and spatial autocorrelation for fires investigated, using Moran's I correlograms. Fire probability models were created using generalized linear models (GLMs). Separate models were produced for abiotic, biotic, anthropogenic and combined factors and an autocovariate variable was tested for model improvement. The hierarchical partitioning method was used to determine independent effects of explanatory variables. The discriminating ability of models was evaluated using area under the curve (AUC) from the receiver operating characteristic (ROC) plot. The results showed that 19.2–24.4% of East Caprivi burned when detected using MODIS hot spots fire data and these fires were strongly spatially autocorrelated. Therefore, the autocovariate variable significantly improved fire probability models when added to them. For autologistic models, i.e. models accounting for spatial autocorrelation, discrimination was good to excellent (AUC 0.858–0.942). For models not counting spatial autocorrelation, prediction success was poor to moderate (AUC 0.542–0.745). The results of this study clearly showed that spatial autocorrelation has to be taken in to account in the fire probability model building process when using remotely sensed and GIS derived data. This study also showed that fire probability models accounting for spatial autocorrelation proved to be superior in regional scale burned area estimation when compared with MODIS burned area product (MCD45A1).     

A framework for identifying spatiotemporal hot spots of speculative land transactions using prospective monitoring procedures: the case of South Korea

Abnormally high-priced transactions in urban land speculation bring detrimental effects on economy, environment, and society. Governmental agencies around the world are striving hard to monitor and control land speculation by introducing various policy objectives and tools for an efficient urban development planning. One of the major challenges in controlling land speculation is to quickly identify the spatiotemporal locations of concern (hot spots) by monitoring the spatial clustering pattern changes over time and to alert the appropriate decision-making agencies for timely policy intervention. In this paper, we introduce a framework to rapidly detect the spatiotemporal hot spots of speculative land transactions in near-real-time data by exploiting the prospective monitoring procedures. We applied this method in the city of Hwasung, Republic of Korea, as an empirical illustration and found that the locations Jeongnam, Bongdam, Mado, and Dongtan were identified as hot spots with high, concentrated transaction values. The results indicate that the proposed framework is a capable tool for capturing prospective temporal indicators and pinpointing the localities of land speculation.     

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.     

Discovering spatial and temporal patterns from taxi-based Floating Car Data: a case study from Nanjing

Floating Car Data (FCD) refers to the trajectories of vehicles equipped with Global Positioning System-enabled devices that automatically record location-related data within a short time interval. As taxies in Chinese cities continually drive along the streets seeking passengers, FCD can easily traverse the entire street network in a city on a daily basis. Taking advantage of this situation, this study extracted passenger pickup and drop-off locations from FCD sourced from 6445 taxis over a 2-week period in Nanjing, China to discover human behavioral patterns and the dynamics behind them. In this study, road nodes are converted to the points, based on which Thiessen polygons are generated to divide the study area into small areas with the goal of exploring the spatial distribution of pickup and drop-off locations. Moran’s I index is used to calculate the spatial autocorrelation of the spatial distribution of pickup and drop-off locations, and hot spot analysis is used to identify statistically significant spatial clusters of hot and cold spots. The spatial and temporal patterns of FCD in the study area are investigated, and the results show that: (1) the temporal patterns show a strong daily rhythm, (2) the spatial patterns show that the number of pickup and drop-off locations gradually diminish from the downtown areas to the outer suburbs, (3) the spatiotemporal patterns exhibit large differences over time, and (4) the driving forces explored by regression models indicate that population density and transportation density are consistent with the population distribution, but per capita disposable income is not consistent with the population distribution.     

基于空间分析的徐州市居民点分布模式研究

居民点空间分布的研究是聚落地理学的主要内容之一,运用空间分析的方法研究居民点的分布能更准确地刻画出其空间分布的本质规律。本文根据2004年TM遥感图像和城市地图得到徐州市城乡居民点空间分布的信息,继而运用样方分析(QA)法、最近邻距离指数(NNI)、K(d)函数、热点探测技术(NNH)研究了徐州市居民点空间分布格局与模式。结果显示:徐州市居民点的空间分布具有明显的空间依赖性,总体上呈现出集聚分布的特点;随着研究尺度的变大,居民点空间分布的集聚性指数也增大;居民点空间分布的热点区域在微观尺度上具有空间随机性、在中观尺度上具有轴带延伸性、宏观尺度上具有面状集中性的特点。     

GRASS GIS-embedded Decision Support Framework for Flood Simulation and Forecasting

This study presents a spatiotemporal analysis tool, called Shyska. This tool allows the simulation and prediction of flash floods in semiarid basins. Shyska has been developed by Geographical Information System (GIS)‐embedded functions, allowing the integration of hydrometeorological information from modern technologies of data acquisition in real time. A Digital Elevation Model (DEM) is used in order to obtain the relevant parameters from the integrated rainfall‐runoff models. Some of its most relevant modules and methodology employed for its development are described. Case studies in basins of south‐east Spain illustrate the applicability of the proposed techniques.     

城市交通热点区域识别及空间交互分析

出租车载客数据可以用于研究居民的出行特征,提取城市的交通热点区域,但对城市交通热点区域的交互关系研究相对较少。本文以纽约市的出租车行程记录数据为数据源,利用交通小区划分结合出租车载客数据提取城市交通热点区域,基于复杂网络的方法对不同日期类型和天气情况的城市交通热点区域空间交互网络进行研究并进行社区发现。结果表明,热点区域受城市核心区的影响而聚集在核心区域周围,城市内社区的形成可以克服地形和行政区域等因素的影响。研究结论有望为城市规划、城市交通管理、出租车调度、以及人们的出行等提供信息参考。     

An Assessment of the Effects of Cell Size on AGNPS Modeling of Watershed Runoff

This study investigates the changes in simulated watershed runoff from the Agricultural NonPoint Source (AGNPS) pollution model as a function of model input cell size resolution for eight different cell sizes (30 m, 60 m, 120 m, 210 m, 240 m, 480 m, 960 m, and 1920 m) for the Little River Watershed (Georgia, USA). Overland cell runoff (area-weighted cell runoff), total runoff volume, clustering statistics, and hot spot patterns were examined for the different cell sizes and trends identified. Total runoff volumes decreased with increasing cell size. Using data sets of 210-m cell size or smaller in conjunction with a representative watershed boundary allows one to model the runoff volumes within 0.2 percent accuracy. The runoff clustering statistics decrease with increasing cell size; a cell size of 960 m or smaller is necessary to indicate significant high-runoff clustering. Runoff hot spot areas have a decreasing trend with increasing cell size; a cell size of 240 m or smaller is required to detect important hot spots. Conclusions regarding cell size effects on runoff estimation cannot be applied to local watershed areas due to the inconsistent changes of runoff volume with cell size; but, optimal cells sizes for clustering and hot spot analyses are applicable to local watershed areas due to the consistent trends.     

A spatiotemporal mining framework for abnormal association patterns in marine environments with a time series of remote sensing images

A spatiotemporal mining framework is a novel tool for the analysis of marine association patterns using multiple remote sensing images. From data pretreatment, to algorithm design, to association rule mining and pattern visualization, this paper outlines a spatiotemporal mining framework for abnormal association patterns in marine environments, including pixel-based and object-based mining models. Within this framework, some key issues are also addressed. In the data pretreatment phase, we propose an algorithm for extracting abnormal objects or pixels over marine surfaces, and construct a mining transaction table with object-based and pixel-based strategies. In the mining algorithm phase, a recursion method to construct a direct association pattern tree is addressed with an asymmetric mutual information table, and a recursive mining algorithm to find frequent items. In the knowledge visualization phase, a “Dimension–Attributes” visualization framework is used to display spatiotemporal association patterns. Finally, spatiotemporal association patterns for marine environmental parameters in the Pacific Ocean are identified, and the results prove the effectiveness and the efficiency of the proposed mining framework.     

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.     

Optimization of spatial statistical approaches to identify land use/land cover change hot spots of Pune region of Maharashtra using remote sensing and GIS techniques

This study investigated land use/land cover change (LULCC) dynamics using temporal satellite images and spatial statistical cluster analysis approaches in order to identify potential LULCC hot spots in the Pune region. LULCC hot spot classes defined as new, progressive and non-progressive were derived from Gi* scores. Results indicate that progressive hot spots have experienced high growth in terms of urban built-up areas (20.67% in 1972–1992 and 19.44% in 1992–2012), industrial areas (0.73% in 1972–1992 and 3.46% in 1992–2012) and fallow lands (4.35% in 1972–1992 and ?6.38% in 1992–2012). It was also noticed that about 28.26% of areas near the city were identified as new hot spots after 1992. Hence, non-significant change areas were identified as non-progressive after 1992. The study demonstrated that LULCC hot spot mapping through the integrated spatial statistical approach was an effective approach for analysing the direction, rate, spatial pattern and spatial relationship of LULCC.     

Evaluating Local Non-Stationarity when Considering the Spatial Variation of Large-scale Autocorrelation

Multi‐scale effects of spatial autocorrelation may be present in datasets. Given the importance of detecting local non‐stationarity in many theoretical as well as applied studies, it is necessary to “remove” the impact of large‐scale autocorrelation before common techniques for local pattern analysis are applied. It is proposed in this paper to employ the regionalized range to define spatially varying sub‐regions within which the impact of large‐scale autocorrelation is minimized and the local patterns can be investigated. A case study is conducted on crime data to detect crime hot spots and cold spots in San Antonio, Texas. The results confirm the necessity of treating the non‐stationarity of large‐scale spatial autocorrelation prior to any action aiming at detecting local autocorrelation.     

Nighttime light data-based analysis of spatiotemporal patterns evolution and driving forces of economic development in China at prefecture level (1992–2020)

Intercity lighting data are an important resource for studying spatial and temporal patterns in regional urban development as an indicator of the intensity of urban social and economic activity. Understanding the evolutionary characteristics of the spatial pattern of regional economic development can support decision-making in regional economic coordination and sustainable development strategies. Based on a long time series of nighttime lighting data from 1992 to 2020, this study used the Theil index, Markoff transfer matrix, spatial autocorrelation, and spatial regression to analyze spatiotemporal evolutionary characteristics and drivers of urban economic development in China. The study found that from 1992 to 2020, China's economic development hot spots have been concentrated in highly developed urban agglomerations namely the Beijing–Tianjin–Hebei region, Shandong Peninsula, Yangtze River Delta, and Pearl River Delta. Cold spots were mainly concentrated in the central-west and southwest of the country. The economic growth rate shows an opposite spatial pattern, which demonstrates the effectiveness of the national coordinated development strategy for regions. The Theil index for urban economic development in China shows an overall downward trend, and the overall economic disparity is mainly due to the relatively low economic development of Tibet, Xinjiang, Gansu, and other western provinces. Therefore, regional economic development remains significantly uneven. In China, the economic type of cities is relatively stable, and the probability of leapfrogging types is low; however, the level of cities with high resource dependence or a single economic structure easily downgrades. The level of economic development and the related socioeconomic factors of neighboring cities influence an obvious spatial spillover effect in the development of urban economies in China. The pattern of China's urban economic development is mainly affected by innovation capacity, financial support, capital investment, transportation infrastructure, and industrial structure.     

基于多视图协同的时空数据可视分析方法

针对时空数据可视分析中存在的问题,提出了由可视分析模型(VAM)和交互协同模型(ICM)组成的多视图协同可视分析模型,将可视分析过程划分为数据变换、数据分析、可视映射和可视绘制4个过程;以可视分析过程中的参数作为协同主题,利用观察者模式,实现关联视图在交互过程中的协同一致;设计了Web环境下,基于"图数协同"和"图图协同"交互模式的多视图协同可视分析架构;构建了犯罪时空数据可视分析原型系统,并利用系统分析了福州市扒窃案的时空分布规律,对涉案价值较高的扒窃案进行了犯罪模式分析。     

顾及属性特征的城市设施热点识别方法

研究城市设施的热点分布对把握当前城市形态具有重要意义。传统的设施热点识别方法容易忽略设施的特征尺度且多以区域识别为主,缺少精准化提取设施热点的方法体系。针对上述问题,本文提出了一种顾及属性特征的设施热点识别方法,并以北京市住宅设施为例进行了试验分析。首先将设施的属性值作为权重,进行加权核密度估计生成密度值表面,利用极值点探测模型提取极值点;然后采用Getis-Ord G_i^*统计进行空间自相关分析,生成具有显著统计学意义的热点区域,筛选极值点得到热点。结果表明,该方法能够准确有效地识别设施热点并进行合理的等级划分,为城市设施空间布局研究提供多样化视角。     

显著局部空间同位模式自动探测方法

局部空间同位模式挖掘旨在揭示多类地理事件在异质环境下的共生共存规律。已有的方法一方面需要模式筛选的频繁度阈值参数,另一方面需要区域探测的划分参数或聚类参数,参数的不合理设置会导致挖掘结果不可靠甚至出现错误。因此,提出了一种显著局部空间同位模式自动探测方法。首先,基于空间统计思想,采用非参数模式重建方法对空间同位模式进行显著性判别,将全局非显著空间同位模式作为进一步局部探测的候选模式;然后,借助自适应空间聚类方法提取每个候选模式的热点区域;最后,通过不断生长并测试每个热点区域,界定显著局部空间同位模式的有效边界,即空间影响域。通过实验与比较发现,该方法能够客观且有效判别空间同位模式的显著性,并且自适应地提取局部同位模式的空间分布结构,降低了现有方法参数设置的主观性。