Modeling spatiotemporal patterns of building vulnerability and content evacuations before a riverine flood disaster

In this paper, a spatiotemporal framework is developed for identifying building vulnerabilities and content evacuations during riverine flooding events. This work investigates the spatiotemporal properties required to trigger building contents evacuations in the floodplain during a flood event. The spatial properties for building risks are based on topography, flood inundation, building location, building elevation, and road access to determine five categories of vulnerability, vulnerable basement, flooded basement, vulnerable first-floor, flooded first-floor, and road access. Using this framework, a model designed to track the spatiotemporal patterns of building evacuations is presented. The model is based upon real-time flood forecast predictions that are linked with building properties to create a model that captures the spatiotemporal ordering of building vulnerabilities and building content evacuations. Applicable to different communities at risk from flooding, the evacuation model is applied to a historical flood for a university campus, demonstrating how the defined elements are used to derive a pattern of vulnerability and evacuation for a campus threatened by severe flooding.     

A lightweight ensemble spatiotemporal interpolation model for geospatial data

ABSTRACT

Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations.     

A spatiotemporal algebra in Hadoop for moving objects

Spatiotemporal data represent the real-world objects that move in geographic space over time. The enormous numbers of mobile sensors and location tracking devices continuously produce massive amounts of such data. This leads to the need for scalable spatiotemporal data management systems. Such systems shall be capable of representing spatiotemporal data in persistent storage and in memory. They shall also provide a range of query processing operators that may scale out in a cloud setting. Currently, very few researches have been conducted to meet this requirement. This paper proposes a Hadoop extension with a spatiotemporal algebra. The algebra consists of moving object types added as Hadoop native types, and operators on top of them. The Hadoop file system has been extended to support parameter passing for files that contain spatiotemporal data, and for operators that can be unary or binary. Both the types and operators are accessible for the MapReduce jobs. Such an extension allows users to write Hadoop programs that can perform spatiotemporal analysis. Certain queries may call more than one operator for different jobs and keep these operators running in parallel. This paper describes the design and implementation of this algebra, and evaluates it using a benchmark that is specific to moving object databases.     

Bayesian analysis of zero inflated spatiotemporal HIV/TB child mortality data through the INLA and SPDE approaches: Applied to data observed between 1992 and 2010 in rural North East South Africa

Longitudinal mortality data with few deaths usually have problems of zero-inflation. This paper presents and applies two Bayesian models which cater for zero-inflation, spatial and temporal random effects. To reduce the computational burden experienced when a large number of geo-locations are treated as a Gaussian field (GF) we transformed the field to a Gaussian Markov Random Fields (GMRF) by triangulation. We then modelled the spatial random effects using the Stochastic Partial Differential Equations (SPDEs). Inference was done using a computationally efficient alternative to Markov chain Monte Carlo (MCMC) called Integrated Nested Laplace Approximation (INLA) suited for GMRF. The models were applied to data from 71,057 children aged 0 to under 10 years from rural north-east South Africa living in 15,703 households over the years 1992–2010. We found protective effects on HIV/TB mortality due to greater birth weight, older age and more antenatal clinic visits during pregnancy (adjusted RR (95% CI)): 0.73(0.53;0.99), 0.18(0.14;0.22) and 0.96(0.94;0.97) respectively. Therefore childhood HIV/TB mortality could be reduced if mothers are better catered for during pregnancy as this can reduce mother-to-child transmissions and contribute to improved birth weights. The INLA and SPDE approaches are computationally good alternatives in modelling large multilevel spatiotemporal GMRF data structures.     

A framework to enhance semantic flexibility for analysis of distributed phenomena

While some geographic phenomena hold uniform properties, such as land‐use zones, many geographic phenomena are distributed such that their properties vary across an extended area. While such distributed phenomena are best represented as continuous surfaces, individual objects (or features) often emerge among clusters of high or low values in a field. For example, areas of relatively high elevation may be viewed as hills, while flat low‐lying areas are perceived as plains in a terrain. A comprehensive spatial analysis of distributed phenomena should examine both the spatial variance of its attribute surfaces and the characteristics of individual objects embedded in the field. An immediate research challenge to meet such spatial analysis needs is that these emerging features often have vague boundaries that vary according to the use and the user. The nature, and even existence, of these objects depend upon the range of values, or thresholds, used to define them. We propose a representation framework that takes a dual raster‐vector approach to capture both field‐ and object‐like characteristics of distributed phenomena and maintain multiple representations of embedded features delineated by boundaries that are likely to be relevant for the expected uses of the data. We demonstrate how boundaries influence the analysis and understanding of spatiotemporal characteristics of distributed phenomena. Using precipitation as a proof of concept, we show how the proposed framework enhances semantic flexibility in spatiotemporal query and analysis of distributed phenomena in geographic information systems.     

基于ZH-1卫星探测的太阳活动低年顶部电离层不规则结构的时空特征

利用ZH-1卫星2019、2020年的原位电子密度观测数据,对卫星观测范围,即地理纬度南北65°之间午夜后顶部电离层的不规则结构进行研究,得到如下结果.(1)午夜后顶部电离层不规则结构集中区主要分布在地磁赤道、中纬度以及较高纬度区,白天赤道异常峰值区为不规则结构的谷值区.(2)不同纬度区不规则结构随地理经度分布呈现出明...     

辽宁冬季气温时空分布特征及其预测概念模型

分析了1961—2004年辽宁冬季平均气温的时空分布特征。结果表明：辽宁冬季平均气温具有明显的南高北低、从沿海向内陆逐渐降低的趋势;冬季平均气温呈线性递增趋势,递增率为0.56 ℃/10 a,与全球气候变暖趋势一致。辽宁冬季气温存在3—5 a和24 a的周期,于1986 a前后发生了一次明显突变,突变前气温呈下降趋势,且以5 a左右的短周期波动为主,突变后开始显著的增温变化,并以10 a以上的长周期变化为主。在此基础上,分析了影响辽宁冬季气温异常的物理因子,建立了辽宁冬季冷暖的预测概念模型。     

Characteristics of stemflow for tall stewartia (Stewartia monadelpha) growing on a hillslope

The characteristics of stemflow were observed in a tall stewartia (Stewartia monadelpha) deciduous forest on a hillslope in central Japan, revealing new findings for a previously unreported type of deciduous forest. Using 2-year observations of 250 rainfall events, we analyzed seasonal and spatial variations in stemflow for several trees, and applied additional data sets of throughfall and plant area index (PAI) to produce a rough estimate of seasonal variations in rainfall redistribution processes and canopy architecture for a single tree. Compared to previous findings for other deciduous tree species, the ratios of throughfall, stemflow, and interception to open-area rainfall obviously varied with PAI changes for tall stewartia. Meteorological conditions of rainfall amount, rainfall intensity, wind speed, and wind direction had little effect on stemflow generation, which was mainly affected by variation in canopy architecture. Three novel characteristics of stemflow were identified for several tall stewartia trees. First, the yearly stemflow ratio at the forest-stand level for tall stewartia (12%) was high compared to previous findings on beech and oak stands, indicating tall stewartia has considerably high potential to generate a great amount of stemflow. Second, stemflow tended to be 1.3–2.0 times greater in the leafed period than in the leafless period. Third, the amount of stemflow was 12–132 times greater on the downslope side of the stem than on the upslope side. It likely caused by the uneven area between the upslope and downslope sides of the canopy and by asymmetrical stemflow pathways between the upslope and downslope sides of the trunk due to downslope tilting of the tree trunk.     

美国阿拉斯加州M_S≥7地震的时空对称性与趋势判断

在时间对称性方面,利用可公度信息提取法和蝴蝶结构图对1970年来阿拉斯加州MS≥7强震进行分析,认为阿拉斯加州2015年发生强震的信号最强。通过对震中空间迁移分析,发现形成的三角形存在关于57°N纬线和150°W经线对称的规律;纬向上存在连续的2次震中位于57°N的北侧,另一次位于57°N南侧为主的迁移规律,经向上有连续的2次震中在150°W东侧,一次在150°W西侧为主的迁移规律。因此判断,未来强震可能向东北方向迁移。同时,由唯象到唯理,对可公度法的物理机理做了研究讨论,为今后国内地震趋势研究提供了参考。     

Modeling relationships between the network distance and travel time dynamics for assessing equity of accessibility to urban parks

Static models of accessibility are usually based on the fixed distance or Average Travel Time (ATT) models. Because of ignoring the traffic as a dynamic process affecting the accessibility through the change of Travel Time (TT), these models lead to unperceived temporal inequities. In contrast to the consideration of the temporal Variation of TT (VTT) in the previous studies, the variation of traffic- related TT and its relations with network distance has not been considered. In this study, relations between VTT and network distance to access urban parks in Tehran megacity has been modeled. Traffic maps at five times of day are used to produce TT maps of Traffic Analysis Zones (TAZs) to their 3-closest parks. Comparison of the Gini coefficients of accessibility show significant inequities of accessibility at different times of day. Relations between the distance, ATT, and TTmax are modeled by statistical analysis. Results show both TT and TTmax have significant positive relations with distance and traffic and reach their maximum at 6 p.m. Observation of significant relations between distance, ATT, TTmax, and VTT provides interesting knowledge for the conversion of temporal measures of equity (TT) to a physical measure of equity (distance). A simple application of these findings for effective management of the spatiotemporal inequities is the definition of critical distances from public services. As an example, to decrease the TTmax of TAZs to less than 12 min, their maximum distance to the closest parks should be less than 4 km. The developed approach can be adopted for the accessibility evaluation of the other public services, particularly the health and education centers.