A Survey of Spatio-temporal Data Mining
2.3 Spatio-temporal Data Mining Applications
Spatio-temporal Data Mining Applications 37
Both trajectories and time series can be represented as sequences, although tra- jectories are sometimes depicted as a matrix with two dimensions corresponding to the row and column IDs of the grid ST field. Each matrix entry value indicates whether the trajectory traverses the corresponding grid region, which is a repre- sentation often used to facilitate the application of CNN models [126, 95]. While graphs can also be represented as matrices, we distinguish between graph and image matrix as separate data representation types. This distinction arises because graph nodes do not follow Euclidean distance like image matrices, leading to entirely different methods for handling each representation type.
Spatial maps can take the form of either graphs or matrices, depending on the specific use case. For instance, in predicting urban traffic flow, data from a city’s transportation network might be depicted as a graph [79]. or a cell region-level traffic flow matrix [96, 115].
Raster data, on the other hand, are usually portrayed as 2D matrices or 3D tensors. When using a matrix, the two dimensions represent locations and time steps, whereas a tensor consists of three dimensions: region, time stamp, and features. Although matrices offer a simpler data representation format compared to tensors, they lack the ability to capture spatial correlation information among locations. Nevertheless, both formats are widely employed for representing raster data [83, 174].
38 A Survey of Spatio-temporal Data Mining
2) Transportation:The transportation sector is another major application do- main for spatio-temporal data mining. In this field, data sources such as GPS traces, traffic sensors, and smartphone data can provide insights into traffic pat- terns, congestion, and mobility. Analyzing this data can help in developing more efficient transportation systems, designing better traffic management strategies, and optimizing public transit routes and schedules. Furthermore, spatio-temporal data mining can assist in predicting and mitigating traffic accidents, facilitating urban planning, and improving overall transportation infrastructure [23, 54]
3) Climate Science:Spatio-temporal data mining is crucial in the study of climate science, where researchers collect and analyze vast amounts of data from weather stations, satellite observations, and climate models. This data typically includes variables such as temperature, precipitation, wind speed, and air pressure. By examining these spatio-temporal datasets, scientists can identify patterns and trends in climate change, monitor extreme weather events, and predict future climate conditions. This information can then inform policy decisions, disaster preparedness, and resource management to help mitigate the impacts of climate change on society and the environment [94, 107].
4) Environmental Science:In environmental science, spatio-temporal data min- ing plays a crucial role in monitoring and understanding the natural world.
Datasets in this field often include remote sensing data, such as satellite imagery, LiDAR, and aerial photography, as well as in-situ measurements from sensors and monitoring stations. Analyzing these datasets allows scientists to study and model complex environmental processes, track land use and land cover changes, and monitor ecosystem health. Moreover, spatio-temporal data mining contributes to the assessment and mitigation of environmental risks, such as pollution, natural disasters, and habitat loss, ultimately helping to guide conservation efforts and sustainable development policies [122].
5) Agriculture:Spatio-temporal data mining is extensively employed in agri- culture for optimizing crop management, monitoring land use, and predicting yield. Remote sensing data, such as satellite images and weather data, provide valuable spatio-temporal information about soil properties, crop growth, and environmental conditions. By analyzing this data, farmers and researchers can identify the most suitable crop varieties for specific regions, predict the optimal planting and harvesting times, and monitor crop health and productivity, thus enhancing agricultural efficiency and sustainability [87].
6) Healthcare: In the healthcare domain, spatio-temporal data mining con- tributes to the understanding and management of diseases, public health monitor-
Spatio-temporal Data Mining Applications 39
ing, and resource allocation. Typical spatio-temporal data in healthcare includes patient records, disease incidence, and environmental factors such as air quality and temperature. Analyzing this data can help identify disease outbreaks, moni- tor the spread of infectious diseases, discover correlations between environmental factors and health conditions, and optimize the distribution of medical resources based on population needs [105].
7) Neuroscience:Spatio-temporal data mining is increasingly important in the field of neuroscience, where researchers collect and analyze complex brain imag- ing data to understand neural activity and cognitive processes. Spatio-temporal data sources in this domain include functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG).
Analyzing these datasets can help researchers identify brain activity patterns, investigate the neural basis of cognitive functions, and diagnose neurological disorders. Furthermore, spatio-temporal data mining techniques can aid in the development of brain-computer interfaces, neurofeedback therapies, and person- alized treatments for various neurological conditions [4].
8) Epidemiology:Epidemiology, the study of the distribution and determinants of health-related states and events, greatly benefits from spatio-temporal data mining. Data sources in this field include disease incidence, patient records, and environmental factors, all with spatial and temporal components. By analyzing this data, epidemiologists can identify disease hotspots, monitor the spread of infectious diseases, and investigate the relationships between environmental factors and health outcomes. Furthermore, spatio-temporal data mining can support the development of effective public health interventions, the optimization of healthcare resource allocation, and the formulation of targeted prevention strategies [88].
9) Crime Data:Spatio-temporal data mining has become an essential tool in the analysis of crime data, as it enables law enforcement agencies and researchers to better understand the dynamics of criminal activity. Crime datasets typically include incident reports, arrest records, and other related information with spatial and temporal attributes. By examining this data, analysts can identify crime patterns, hotspots, and trends, as well as evaluate the effectiveness of policing strategies and interventions. Moreover, spatio-temporal data mining can support predictive policing, allowing law enforcement agencies to deploy resources more efficiently, develop targeted crime prevention measures, and ultimately enhance public safety [123].
40 A Survey of Spatio-temporal Data Mining
10) Video Processing:In the domain of video processing, spatio-temporal data mining plays a significant role in extracting valuable insights and patterns from large volumes of video data. The typical ST data in this domain consists of sequences of images or frames captured over time, which together form a video.
Each frame contains spatial information in the form of pixel values, while the temporal aspect is captured through the chronological order of frames. Video processing in the context of STDM can be employed for various purposes, such as video summarization, object tracking, motion detection, and activity recognition.
By analyzing the spatio-temporal patterns within video data, researchers and practitioners can develop algorithms to identify and classify objects or actions, detect and track movement, and even predict future events or behaviors. This application domain is particularly relevant in areas such as surveillance, sports analytics, traffic management, and human-computer interaction [81, 121].