Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

/pdf/spatiotemporal-data-mining-a-survey-on-challenges-and-open-3eyjcbsma4.pdf

Spatiotemporal data mining: a survey on challenges and open problems.

Images are particular and well-known instances of spatial big data. Typically spatial data are scale specific and in this paper, we propose mechanisms to effectively address issues of scale in the analysis of images. We focus on spatial data extracted from images using the Discrete Pulse Transform (DPT). The DPT extracts discrete pulses from images at multiple scales that are recognisable as connected components. Traditionally, fractals are used for this purpose, but they fall short as the process underlying fractality is usually either absent or poorly understood. This paper investigates the H t -index (head/tail break) as an alternative, merging ideas from image analysis and spatial statistics. More specifically, we use the H t -index for the analysis of anisotropic point patterns that are obtained from applying the DPT. We propose a multi-level H t -index decomposition in this regard. This is the first mechanism for the DPT enabling an informed partition of the scale-space. The results show that the H t -index is well suited to identify the anisotropic structure location within specific scales and thereby substantially reduces computational costs. We conclude that the use of the H t -index is promising and is well-suited for the further analysis of spatial big data.

Use of fractals to measure anisotropy in point patterns extracted with the DPT of an image

Geospatial databases are basic tools to collect, index, and manage georeferenced data indicators in sustainability research for efficient, long-term analysis. NoSQL databases are increasingly applied to manage the ever-growing massive spatial vector data (SVD) with their changeable data schemas, agile scalability, and fast query response time. Spatial queries are basic operations in geospatial databases. According to Green information technology, an efficient spatial index can accelerate query processing and save power consumption for ubiquitous spatial applications. Current solutions tend to pursue it by indexing spatial objects with space-filling curves or geohash on NoSQL databases. As for the performance-wise R-tree family, they are mainly used in slow disk-based spatial access methods on NoSQL databases that incur high loading and searching costs. Therefore, performing spatial queries efficiently with the R-tree family on NoSQL databases remains a challenge. In this paper, an in-memory balanced and distributed R*-tree index named the BDRST index is proposed and implemented on HBase for efficient spatial-query processing of massive SVD. The BDRST index stores and distributes serialized R*-trees to HBase regions in association with SVD partitions in the same table. Moreover, an efficient optimized server-side parallel processing framework is presented for real-time R*-tree instantiation and query processing. Through extensive experiments on real-world land-use data sets, the performance of our method is tested, including index building, index quality, spatial queries, and applications. Our proposed method outperforms other state-of-the-art solutions, saving between 27.36% and 95.94% on average execution time for the above operations. Experimental results show the capability of the BDRST index to support spatial queries over large-scale SVD, and our method provides a solution for efficient sustainability research that involves massive georeferenced data.

/pdf/improving-nosql-spatial-query-processing-with-server-side-in-3twvguga.pdf

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

Spatiotemporal Data Mining: A Survey on Challenges and Open Problems

With the continuous acceleration of China’s urbanization process, color steel plate, as a new type of building material, has been widely used in all kinds of temporary buildings and has become the spatial carrier of the specific development stage of urbanization. This study focuses on Lanzhou City as a case study to deeply analyze the spatiotemporal distribution and evolution of color steel plate buildings. Utilizing data extracted from Google imagery and GF-2 satellite images of the built-up areas in Lanzhou, spatial statistical and analytical methods such as centroid analysis, compactness index, and patch density are applied. Systematic analysis is conducted across different time periods and spatial scales to examine the evolution of indicators, including quantity, centroid distribution, spatial clustering, and distribution direction. The results show that from 2013 to 2021, the prevalence of color steel buildings in Lanzhou city initially increased and then decreased, and the number peaked in 2017, but there is a significant difference between distinct areas in the urban area. By quantitatively analyzing the spatial and temporal evolution characteristics of color steel plate buildings, this study reveals the important role it plays in promoting the urbanization process and provides a scientific basis for relevant planning decisions. 

Spatial-Temporal Evolution Characteristics Analysis of Color Steel Buildings in Lanzhou City

With the rapid development of geospatial data acquisition and processing technology, the scale of spatial data is expanding. Mass production applications put forward higher requirements for the performance of geospatial data analysis. In this study, we developed a geospatial big data analytics engine based on SuperMap iObject for Java and Apache Spark. The geospatial big data analytics engine can increase the RDD representation ability of spatial data. The spatial indexing can make the spatial calculation on the nodes of the Spark cluster distributed and efficient. The experimental results show that compared with the traditional algorithm, the geospatial big data analytics engine for Spark has better execution efficiency.

Geospatial Big Data Analytics Engine for Spark

Spatial visual analytics 1 is a critical aspect for big spatiotemporal data (BSTD) in exhibition the hidden spatiotemporal patterns. However, the real-time and dynamic characters of BSTD causes great challenges for the GIS domain and big data domain due to the limitation of the current visual analytics tools. Thus, we propose and implement a visual analytics framework. The framework integrates open source map library and visualization library to provide innovative visual capacity for BSTD. The framework uses GIScript and iDesktop Cross to support high performance BSTD spatial analytics. The application of the framework in global air traffic data proves its efficiency and utility in discovering the global flight patterns. The framework simplifies the visual analytics procedure for BSTD and can be adopted by various domains.

A Visual Analytics Framework for Big Spatiotemporal Data

Visual analytics 1 for spatiotemporal data is an essential issue in shown the patterns of the spatial data mining results. To deal with challenges caused by dynamic spatiotemporal data require efficient visual analytics that visualizes real-time and dynamic spatial data. We proposed and implemented an integrated visual analytics framework. It integrated open source map library, visual library, and modern web development technology. It made use of Spark Streaming in real-time data processing while real-time mapping results on the DataFlowLayer. Visual analytics framework for dynamic objects is built based on high-performance processing and hardware mixed acceleration strategies. Benchmark experiments showed that it achieved excellent performance for visualizing spatiotemporal data.

An Integrated Visual Analytics Framework for Spatiotemporal Data

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Geospatial Big Data Analytics Engine for Spark

A Visual Analytics Framework for Big Spatiotemporal Data

An Integrated Visual Analytics Framework for Spatiotemporal Data