“stppSim”: A Novel Analytical Tool for Creating Synthetic Spatio-Temporal Point Data

In crime science, understanding the dynamics and interactions between crime events is crucial for comprehending the underlying factors that drive their occurrences. Nonetheless, gaining access to detailed spatiotemporal crime records from law enforcement faces significant challenges due to confidentiality concerns. In response to these challenges, this paper introduces an innovative analytical tool named “stppSim,” designed to synthesize fine-grained spatiotemporal point records while safeguarding the privacy of individual locations. By utilizing the open-source R platform, this tool ensures easy accessibility for researchers, facilitating download, re-use, and potential advancements in various research domains beyond crime science.

MANAGEMENT OF SPATIO-TEMPORAL DATA OF CADASTRAL INFORMATION SYSTEM IN CHINA

Cadastral Information System (CIS) is designed for the office automation of cadastral management. With the development of the market economics in China, cadastral management is facing many new problems. The most crucial one is the temporal problem in cadastral management. That is, CIS must consider both spatial data and temporal data. This paper reviews the situation of the current CIS and provides a method to manage the spatiotemporal data of CIS, and takes the CIS for Guangdong Province as an example to explain how to realize it in practice.

Constructing a raster-based spatio-temporal hierarchical data model for marine fisheries application

Marine information has been increasing quickly. The traditional database technologies have disadvantages in manipulating large amounts of marine information which relates to the position in 3-D with the time. Recently, greater emphasis has been placed on GIS （geographical information system）to deal with the marine information. The GIS has shown great success for terrestrial applications in the last decades, but its use in marine fields has been far more restricted. One of the main reasons is that most of the GIS systems or their data models are designed for land applications. They cannot do well with the nature of the marine environment and for the marine information. And this becomes a fundamental challenge to the traditional GIS and its data structure. This work designed a data model, the raster-based spatio-temporal hierarchical data model （RSHDM）, for the marine information system, or for the knowledge discovery fi＇om spatio-temporal data, which bases itself on the nature of the marine data and overcomes the shortages of the current spatio-temporal models when they are used in the field. As an experiment, the marine fishery data warehouse （FDW） for marine fishery management was set up, which was based on the RSHDM. The experiment proved that the RSHDM can do well with the data and can extract easily the aggregations that the management needs at different levels.

Spatio-temporal changes of underground coal fires during 2008-2016 in Khanh Hoa coal field(North-east of Viet Nam) using Landsat time-series data

Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.

Fusing multi-source data to map spatio-temporal dynamics of winter rape on the Jianghan Plain and Dongting Lake Plain, China

Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role in the cooking oil market of China. The Jianghan Plain and Dongting Lake Plain (JPDLP) are major agricultural production areas in China. Essential changes in winter rape distribution have taken place in this area during the 21st century. However, the pattern of these changes remains unknown. In this study, the spatial and temporal dynamics of winter rape from 2000 to 2017 on the JPDLP were analyzed. An artificial neural network (ANN)-based classification method was proposed to map fractional winter rape distribution by fusing moderate resolution imaging spectrometer (MODIS) data and high-resolution imagery. The results are as follows:(1) The total winter rape acreages on the JPDLP dropped significantly, especially on the Jianghan Plain with a decline of about 45% during 2000 and 2017.(2) The winter rape abundance keeps changing with about 20–30% croplands changing their abundance drastically in every two consecutive observation years.(3) The winter rape has obvious regional differentiation for the trend of its change at the county level, and the decreasing trend was observed more strongly in the traditionally dominant agricultural counties.

Construction of Smart City Spatio-Temporal Information Cloud Platform in Weifang,China

On the basis of the digital Weifang geospatial framework,Smart Weifang spatio-temporal information cloud platform(WFCP)integrated legal person information,population,place name and address data,macroeconomic data and so on.And it also expanded the data contents,such as the indoor and outdoor data,the overground and underground data,panoramic data and real data.It also introduced the contents of historical geographical information in different periods and real-time location information,address information of sensing equipment,real-time perception and interpreting information.It has overcome the difficulties of real-time access of Internet of Things(IoT)perception,multi-node collaboration,64-bit support,cluster deployment and has the characteristics of spatio-temporal management,ondemand service,large data analysis and micro-service architecture.It built spatio-temporal information big data center and spatio-temporal information cloud platform,realized the convergence and management of the distributed big data,deeply applied for land,transportation,environmental protection,police and subdistrict five areas,by supporting the integrated application of multi-source information and supporting intelligent deep application.In the aspect of hardware environment construction,according to the top-level design and unified arrangement of Smart Weifang,the WFCP was migrated to Weifang cloud computing center,to achieve the on-demand computing resources and dynamic scheduling load-based computing resources,to support the generalizing load map application.

Spatio-temporal model for soil characteristic of reclamation land

The development of spatio-temporal data model is introduced. According to the soil characteristic of reclamation land, we adopt the base state with amendments model of multi-layer raster to organize the spatio-temporal data, using the combined data structure on linear quadtree and linear octree to code. The advantage of this model is that it can easily obtain the information of certain layer and integratedly analyze the data with other methods. Then, the methods of obtain and analyses are introduced. The method can provide a tool for the research of the soil characteristic change and spatial distribution in reclamation land.

基于时空数据融合的塔吉克斯坦中高时空分辨率NDVI数据集(2010-2020)

归一化植被指数(Normalized Difference Vegetation Index,NDVI)是研究植被最常用的遥感指数之一。NDVI长时间序列数据对于植被变化研究有着重要的意义。然而由于传感器的限制,遥感数据的时间分辨率与空间分辨率不能兼顾,因此在目前广泛使用的NDVI数据产品中,高时空分辨率的数据还较为缺乏。本产品基于Cubist模型对MODIS数据与Landsat及哨兵等遥感数据进行时空数据融合,得到了塔吉克斯坦2010–2020年中高时空分辨率Landsat-MODIS融合数据,以及2020年中高时空分辨率Sentinel-MODIS融合数据。为保证数据的准确性和可靠性,本数据集从数据源的质控,模型训练优化,以及模型独立验证三个方面对数据产品进行质量控制,且取得了较好的验证效果。本数据集可反映塔吉克斯坦2010–2020年NDVI时空变化情况,可为该地区植被变化分析、生态环境监测等提供长时间序列数据支撑。

面向源-目的地流的多元时空数据可视分析

交通智能(IC)卡可以记录居民的移动出行,反映居民的源-目的地(OD)信息;但智能卡记录的OD流数据规模大,直接可视化空间分布容易导致视觉杂乱,并且多元数据类型多,更难以和流数据结合对比分析。首先,针对直接可视化大规模OD数据的空间分布容易视觉遮挡的问题,提出基于正交非负矩阵分解(ONMF)的流聚类方法。所提方法对源-目的地数据聚类后再可视化,可以减少不必要的遮挡。然后,针对多元时空数据类型多难以结合对比分析的问题,设计了公交站点多元时序数据视图。该可视化方法将公交站点的流量大小和空气质量、空气温度、相对湿度、降雨量这四类多元数据在同一时间序列上编码,提高了视图的空间利用率并且可以对比分析。再次,为了辅助用户探索分析,开发了基于OD流和多元数据的交互式可视分析系统,并设计了多种交互操作提升用户探索效率。最后,基于新加坡交通智能卡数据集,从聚类效果和运行时间对该聚类方法评估。结果显示,在用轮廓系数评估聚类效果上,所提方法比原始方法提升了0.028,比用K均值聚类方法提升了0.253;在运行时间上比聚类效果较好的ONMFS(ONMF through Subspace exploration)方法少了254 s。通过案例分析和系统功能对比验证了系统的有效性。

ST-WaveMLP:面向交通流量预测的时空全局感知网络模型

交通流量预测在智能交通系统中起着至关重要的作用。精准的交通流量预测不仅能帮助城市管理者进行更好的交通管理,也能帮助人们制定合适的出行计划。然而精准预测交通流量颇具挑战性,主要难点在于如何捕获交通流量数据中复杂的时空依赖性。近年来,深度学习方法已被成功应用于网格交通流量预测,主要采用深度卷积神经网络来捕获时空依赖性。但是卷积神经网络主要关注数据中空间特征的提取与整合,难以充分挖掘其中复杂的时空依赖性,而且单层卷积网络只能捕获局部空间依赖,因此,要想捕获全局空间依赖就需要对超多层的卷积网络进行堆叠,这将使整个网络模型训练收敛速度变慢。为了解决些问题,提出了一种面向交通流量预测的全局感知时空网络模型ST-WaveMLP,主要使用以多层感知机(MLP)为基础的可重复结构ST-WaveBlock来捕获相关的时空依赖。ST-WaveBlock中包含了捕获全局空间依赖和局部时间依赖的模块(SGAC),以及用于捕获局部空间依赖和全局时间依赖的模块(SLAC)。ST-WaveBlock具有较强的时空表征学习能力,通常仅用2~4个ST-WaveBlock堆叠就能有效捕获数据中的时空依赖性。最后,在4个实际交通流量数据集上进行实验验证,结果表明ST-WaveMLP具有更好的收敛性以及更高的预测精度,相较于之前最好的方法,所提方法预测精度的提升最高可达9.57%,模型收敛速度的提升最高可达30.6%。

面向社区生活圈的行为—设施时空匹配度评价--以南京市中心城区为例

从居民日常行为视角评估设施供需关系,是诊断生活圈范围内设施布局不合理问题的重要手段。已有研究主要从空间视角入手,较少考虑时间维度上的动态变化。基于居民行为活动差异进行时间段划分,利用POI数据和手机信令数据,提出一种面向社区生活圈的行为—设施时空匹配度评价思路,并以南京市中心城区为例进行研究。结果表明:尽管空间维度上设施供给、居民活动表现为显著的正相关,但时间维度上,二者出现明显的时空分异,部分时间段出现匹配错位情况。提出既可评估设施布局和居民活动的空间匹配模式,也可反映时间维度变化的方法,更综合地识别设施供需匹配上存在的问题,从而支撑设施时空配置和居民活动引导的策略。

2021-2022年鄱阳湖丰水期水体富营养化指标空间分布数据集

鄱阳湖是长江流域最大的通江湖泊,对维持长江流域生态功能具有举足轻重的作用。然而随着气候变化和人类活动的共同影响,其富营养化程度逐年增加,生态功能逐渐恶化。为研究鄱阳湖富营养化的时空变化特征,保护鄱阳湖水资源、水环境安全,于2021年和2022年夏季藻类生长高峰期基于野外采样规范均匀采集了鄱阳湖湖体72个点位的水样,在实验室严格按照国标和行业标准规定的方法完成了采样点水体富营养化指标总氮、总磷、高锰酸盐指数和叶绿素的测定,形成了鄱阳湖2021-2022年丰水期水体富营养化指标空间分布数据集。本数据集产品可准确表征鄱阳湖丰水期水体污染状况、富营养化状态及其在空间上的分布特征,对鄱阳湖流域污染管控,长江流域水环境保护具有重要的数据支撑意义。

基于NPP-VIIRS的胶东经济圈GDP时空演变及空间化模拟研究

为研究胶东经济圈经济发展的空间差异,以2012-2022年NPP-VIIRS夜间灯光数据和GDP数据为数据源,运用探索性空间分析方法探索GDP时空演变特征,采用指数平滑、空间建模和逐像元线性矫正等方法进行GDP空间化模拟和预测.结果表明:1)胶东经济圈最佳夜间灯光指数和GDP高度相关,有相似的空间分布和演变规律;2)经济欠活跃型区域逐步缩减,经济一般型区域空间分布由带状逐渐转为片状,GDP的空间聚集性趋于减弱;3)GDP热点区主要在青岛的黄岛区和胶州市,冷点区主要在潍坊的部分县区且逐渐收缩;4)指数平滑后的夜间灯光数据与GDP相关性显著增强,更适合进行拟合;5)GDP空间化模拟相对误差小于1%,模拟效果较好;6)高密度GDP主要分布在沿海县区和城市中心城区,并在各县区的交通线节点上零星出现.

基于耦合协调分析的火灾-经济-环境时空演化研究

为探究火灾、经济、环境的时空演化特征,基于2000—2019年中国火灾、经济、环境数据,建立火灾-经济-环境耦合协调模型,研究火灾、经济、环境系统的耦合发展水平;结合探索性空间数据分析(ESDA)方法研究我国31个省域各子系统时空演变特征及聚集性,并基于Moran'I检验开展火灾、经济、环境空间相关性分析。研究结果表明:2000—2019年火灾、经济和环境综合发展耦合良好。从时间维度上,2000—2019年耦合度和协调度呈上升趋势,耦合度受火灾综合得分影响,协调度受经济子系统影响;从空间维度上,经济发展是影响31个省域耦合协调度的主要因素。从耦合关系时空演变分析看,省域耦合度随时间呈聚集状态,耦合协调度在空间聚集中呈随机性。火灾、经济和环境对耦合协调的不同影响,导致协调类型及空间聚集状态不同。

基于时序NPP-VIIRS夜间灯光和土地利用数据的福州市GDP时空分布研究

基于时序NPP-VIIRS夜间灯光、社会经济统计等数据,构建GDP空间化模型,并结合空间分辨率为30 m×30 m的土地利用数据对模型进行修正,研究2013—2020年福州市经济空间差异性及演变趋势。结果表明:(1)利用土地利用数据对GDP空间化模型进行修正以提高单元网格精度和效度的方法是可行的,对较小尺度的经济空间分析具有意义。(2)将土地利用数据对应的各产业产值用于对GDP空间化模型修正,能够进一步提升单元网格的模拟精度。(3)从共时性比对来看,福州市的经济空间呈现东部GDP较高,西部和北部GDP以低值为主、高值零星分布的格局。从历时性来看,自2015年起,福州市GDP高值区域的扩张与GDP总值的增长速度明显提高,2018年后整体出现略微下降趋势。

基于ST-DBSCAN聚类算法的矿井冲击地压微震监测数据时空特性分析

矿井冲击地压是地下矿山中的一种常见地质灾害,对矿山的安全生产和员工的人身安全造成威胁。矿井常规的数据分析多采用数理统计方法进行危险预警分析,具有一定的片面性和局限性,对高维的微震监测数据时空特性分析明显不足,数据分析缺乏实时性,预警分析对特定软件的依赖性过大。为了提高冲击地压的预警能力,提出了一种基于ST-DBSCAN时空聚类算法的方法来分析矿井冲击地压微震监测数据的时空特性,相对于传统聚类算法,该方法表现出更高的鲁棒性。通过矿井真实微震数据验证分析,证明了ST-DBSCAN时空聚类算法能够满足冲击地压微震监测数据时、空、强分析要求,可以识别和分类工作面回采过程中的应力增高区,能够为矿井冲击地压的预警和管理提供决策支持。

基于ERA5-Land产品的黄河流域蒸散时空变化特征

近年来,黄河流域气候发生明显变化,对流域地表水文、生态等过程产生显著影响。研究黄河流域蒸散时空变化特征,对加深陆-气相互作用认识及区域水资源管理有重要意义。本文分别在黄河源区、河套地区以及黄河下游地区选取一个代表性站点[海北、SACOL(Semi-Arid Climate and Environ⁃ment Observatory of Lanzhou University)和禹城站]的观测资料,检验欧洲中期天气预报中心ERA5-Land产品中蒸散资料在黄河流域的适用性。在此基础上,利用ERA5-Land资料中1980—2021年逐月潜热通量资料,结合经验正交函数分解(Empirical Orthogonal Function,EOF)、功率谱和回归分析研究近42 a黄河流域蒸散时空变化特征。结果表明:ERA5-Land资料能够反映海北、SACOL和禹城站蒸散变化特征,与观测资料的相关性较好,偏差和均方根误差较小,适用于黄河流域蒸散时空变化特征分析。黄河流域不同区域蒸散存在多时间尺度变化,显著振荡周期主要为5、15 a,有明显的年际和年代际变化。黄河流域不同区域年蒸散EOF分解第1模态表现出同位相变化,在2004年前后由增大转为减少趋势;第2模态为偶极子分布,空间分布表现反向变化特征。近42 a黄河流域年蒸散为明显减少趋势,不同区域减幅不同,下游减少速率最快,为-3.74 mm·a^(-1),河套地区为-2.82 mm·a^(-1),黄河源区减少速率相对平缓。夏季蒸散变率最大,河套和下游减少速率较大;冬季蒸散变率较小,黄河源区减少速率最大,为-0.48 mm·a^(-1)。

基于多特征融合的GraphHeat-ChebNet隧道形变预测模型

对隧道的形变进行预测是隧道结构异常检测的内容之一。为了充分挖掘形变特征的时空关联性,针对隧道内衬多个断面的形变同时预测,提出一种基于多特征融合的GraphHeat-ChebNet隧道形变预测模型。所提模型中利用GraphHeat和ChebNet这2种图卷积网络(graph convolution net,GCN)分别提取特征信号的低频和高频部分,并获取形变特征的空间关联性,ConvGRUs网络用于提取特征在时间上的关联性,通过三阶段融合方法保留挖掘的信息。为了解决实验数据在时间维度上不充足的问题,引入双层滑动窗口机制。此外,所提模型与其他模型或算法在不同数据集上实验比较,衡量一天和两天预测值的误差指标优于其他模型,而且对大部分节点预测的误差较低。说明模型受样本节点数影响较小,能较好地预测一天和两天的形变,模型学习特征与时空模式的能力较强,泛化性较好。

考虑数据中心用能时空可调的电-气互联综合能源系统分布式光伏最大准入容量计算

在数字经济背景下,数据中心日益增长的用能规模和灵活迅速的负荷调节能力引起了广泛关注。为促进分布式清洁能源的消纳,本文提出一种考虑数据中心用能时空可调的电-气互联综合能源系统分布式光伏最大准入容量计算方法。首先,对数据中心能耗及运行特性进行建模,并分析数据中心用能时空可调特性;然后,在计及电-气能源网络安全约束的前提下,构建集成数据中心的电-气互联综合能源系统分布式光伏最大准入容量优化模型,利用凸松弛技术和增量分段线性化方法将模型转化为混合整数二阶锥模型求解。最后,在97节点电网和11节点气网算例中,验证了考虑数据中心用能时空可调特性对分布式光伏消纳的促进作用,并分析了电-气能源网络安全约束对分布式光伏最大准入容量的影响。

基于L-DCGAN的风机齿轮箱时序温度数据生成模型

针对DCGAN网络生成的数据独立同分布无法很好地生成随时间演变自相关特性数据的问题,文章在原有网络上加入处理时序数据有着良好表现的LSTM网络构建新的生成网络和判别网络,得到改进的生成对抗网络L-DCGAN。在利用CNN自适应特征提取能力提高数据质量的同时,利用LSTM融合输出变量与其他输入变量在时间维度上的时序关联性,从而准确追踪数据的时间变化趋势,生成具有时间自相关特性的数据。以风机实际运行数据对模型进行验证,同时利用统计学以及结构相似性指标分别对DCGAN以及本文模型生成数据的真实性和相似性进行评估,评估结果表明,本文模型能够生成更加准确且相关性更高的数据。