Understanding urban heat exposure dynamics is critical for public health, urban management, and climate change resilience. Near real-time analysis of urban heat enables quick decision-making and timely resource allocation, thereby enhancing the well-being of urban residents, especially during heatwaves or electricity shortages. To serve this purpose, we develop a cyberGIS framework to analyze and visualize human sentiments of heat exposure dynamically based on near real-time location-based social media (LBSM) data. Large volumes and low-cost LBSM data, together with a content analysis algorithm based on natural language processing are used effectively to generate near real-time heat exposure maps from human sentiments on social media at both city and national scales with km spatial resolution and census tract spatial unit. We conducted a case study to visualize and analyze human sentiments of heat exposure in Chicago and the United States in September 2021. Enabled with high-performance computing, dynamic visualization of heat exposure is achieved with fine spatiotemporal scales while heat exposure detected from social media data can be used to understand heat exposure from a human perspective and allow timely responses to extreme heat.HIGHLIGHTSNear real-time and high spatial resolution mapping of human sentiments of heat exposure with Twitter dataAn integrated cyberGIS and machine learning framework for visualizing heat exposure with Twitter dataHuman sentiment of heat exposure mapping in the City of Chicago and the United States

, a system that supports efficient spatial queries for complex 3D objects. 3DPro uses progressive compression of 3D objects preserving multiple levels of details, which significantly reduces the size of the objects and has the data fit into memory. Through a novel Filter-Progressive-Refine paradigm, 3DPro can have query results returned early whenever possible to minimize decompression and geometric computations of 3D objects in higher resolution representations. Our experiments demonstrate that 3DPro out-performs the state-of-the-art 3D data processing techniques by up to an order of magnitude for typical spatial queries.

With the widespread use of mobile and sensing devices, there has been an explosion of high velocity, transient data having spatial and temporal characteristics. Interactive analysis at such scale and speed require support for highly efficient query processing backend frameworks coupled with lightweight yet powerful frontend interfaces. While existing in-memory distributed stream processing frameworks are perfect candidates for scalable big data processing, spatio-temporal systems in this domain are mostly dominated by specify-once-apply-continuously query model. Any modification in query state requires query restart limiting system responsiveness and producing outdated or in the worst case erroneous results. Furthermore, most of the contemporary spatio-temporal big data systems are designed to operate in a single execution environment limiting their applicability to users accustomed to other similar frameworks with different APIs. In this paper, we demon-strate SPEAR-Board; an interactive web-based interface integrated with cross-platform stream processing engine; SPEAR, capable of seamlessly handling spatio-temporal query state changes in real-time. We demonstrate working of SPEAR-Board with respect to spatio-temporal Range and Nearest Neighbor queries backed by Apache Spark and Apache Flink deployed over cloud resources.

Contact tracing is gaining its importance in controlling the spread of COVID-19. However, the enormous volume of the frequently sampled tracing data brings major challenges for real-time processing. In this paper, we propose a GPU-based real-time contact tracing system based on spatial proximity queries with temporal constraints using location data. We provide dynamic indexing of moving objects using an adaptive partitioning schema on GPU with extremely low overhead. Our system optimizes the retrieval of contacted pairs to match both the requirements of contact tracing scenarios and GPU centered parallelism. We propose an efficient contacts evaluation mechanism to keep only the spatially and temporally valid contacts. Our experiments demonstrate that the system can achieve sub-second level response for large-scale contact tracing of tens of millions of people, with two magnitudes of performance boost over CPU based approach.

With the advent of IoT and emerging 5G technology, real-time streaming data are being generated at unprecedented speed and volume, and coming with both temporal and spatial dimensions. Effective analysis at such scale and speed requires support for dynamically adjusting querying capabilities in real-time. In spatio-temporal domain, this warrants for data as well as query optimization strategies especially for objects with changing motion states. Contemporary spatio-temporal data stream management systems in distributed domain are mostly dominated by specified-once-applied-continuously query model. Any modification in query state requires query restart limiting system responsiveness and producing outdated or in worst case erroneous results. In this paper, we propose adaptations of principles from streaming databases, spatial data management and distributed computing to support dynamic spatio-temporal query processing over high velocity big data streams. We first formulate a set of spatio-temporal data types and functions to seamlessly handle changes in distributed query states. We develop a comprehensive set of streaming spatio-temporal querying methods, and propose geohash based dynamic spatial partitioning for effective parallel processing. We implement a prototype on top of Apache Flink, where the in-memory stream processing fits nicely with our spatio-temporal models. Comparative evaluation of our prototype demonstrates the effectiveness our strategy by maintaining high consistent processing rates for both stationary as well as moving queries over high velocity spatio-temporal big data streams.

Geometric computation can be heavy duty for spatial queries, in particular for complex geometries such as polygons with many edges based on a vector-based representation. While many techniques have been provided for spatial partitioning and indexing, they are mainly built on minimal bounding boxes or other approximation methods, which will not mitigate the high cost of geometric computation. In this paper, we propose a novel vector-raster hybrid approach through rasterization, where pixel-centric rich information is preserved to help not only filtering out more candidates but also reducing geometry computation load. Based on the hybrid model, we develop an efficient rasterization based ray casting method for point-in-polygon queries and a circle buffering method for point-to-polygon distance calculation, which is a common operation for distance based queries. Our experiments demonstrate that the hybrid model can boost the performance of spatial queries on complex polygons by up to one order of magnitude.

Spatial cross-matching operation over geospatial polygonal datasets is important to a variety of GIS applications. However, it involves extensive computation cost associated with intersection and union of a geospatial polygon pair from large scale datasets. This mandates for exploration of parallel computing capabilities such as GPU to increase the efficiency of such operations. In this paper, we present a CPU-GPU hybrid platform to accelerate the cross-matching operation of geospatial datasets. The computing tasks are dynamically scheduled to be executed either on CPU or GPU. To accommodate geospatial datasets processing on GPU using pixelization approach, we convert the floating point-valued vertices into integer-valued vertices with an adaptive scaling factor as a function of area of minimum bounding box. We test our framework over Natural Earth Dataset and achieve 10x speedup on NVIDIA GeForce GTX750 GPU and 14x speedup on Tesla K80 GPU over 280,000 polygon pairs in one tile and 400 tiles in total. We also investigate the effects of input data size to the IO / computation ratio and note that the sufficiently large input data size is required to better utilize the computing power of GPU. Finally, with comparison between two GPUs, our results demonstrate that the efficient cross-matching comparison can be achieved with a cost-effective GPU.

Much effort has been devoted to support high performance spatial queries on large volumes of spatial data in distributed spatial computing systems, especially in the MapReduce paradigm. Recent works have focused on extending spatial MapReduce frameworks to leverage high performance in-memory distributed processing capabilities of systems such as Spark. However, the performance advantage comes with the requirement of having enough memory and comprehensive configuration. Failing to fulfill this falls back to disk IO, defeating the purpose of such systems or in worst case gets out of memory and fails the job. The problem is aggravated further for spatial processing since the underlying in-memory systems are oblivious of spatial data features and characteristics. In this paper we present SparkGIS - an in-memory oriented spatial data querying system for high throughput and low latency spatial query handling by adapting Apache Spark's distributed processing capabilities. It supports basic spatial queries including containment, spatial join and k-nearest neighbor and allows extending these to complex query pipelines. SparkGIS mitigates skew in distributed processing by supporting several dynamic partitioning algorithms suitable for a rich set of contemporary application scenarios. Multilevel global and local, pre-generated and on-demand in-memory indexes, allow SparkGIS to prune input data and apply compute intensive operations on a subset of relevant spatial objects only. Finally, SparkGIS employs dynamic query rewriting to gracefully manage large spatial query workflows that exceed available distributed resources. Our comparative evaluation has shown that the performance of SparkGIS is on par with contemporary Spark based platforms for relatively smaller queries and outperforms them for larger data and memory intensive workflows by dynamic query rewriting and efficient spatial data management.