International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 10 Issue: 08 | Aug 2023 www.irjet.net p-ISSN: 2395-0072 © 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 219 Spatio-Temporal Data Analysis using Deep Learning Arijeet Singh 1 , Sajid Javid 2 1 Student, Department Of Computer Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University 2 Student, Department Of Computer Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - This survey aims to present a thorough overview of the various spatio-temporal data analysis applications of deep learning techniques. The study examines the widely used applications of spatiotemporal data analysis, including transportation, social media events, environmental concerns, human mobility, action recognition, and other related areas. Convolutional Neural Networks, Recurrent Neural Networks, Graph Convolutional Networks, and other neural network architectures are just a few examples of the deep learning algorithms and neural network architectures we discuss in this article for the various spatio-temporal data domains listed above. Key Words: Deep Learning, Spatio-Temporal, Neural Network, Metrics, Network Architecture 1.INTRODUCTION Spatio-temporal data analytics is an interdisciplinary field that deals with the analysis and modelling of data that has both spatial and temporal characteristics. This type of data is commonly generated in various domains such as environmental sciences, geography, transportation, and urban planning, among others. Deep learning, a subset of machine learning, has received a lot of attention lately because of its ability to handle large quantities of complex and multidimensional data. In particular, deep learning has shown to be highly effective in handling spatial-temporal data. The following are some benefits of using Deep Learning models for Spatio Temporal data analysis over more conventional techniques[1]: Learning hierarchical feature representations automatically: Deep Learning models can learn these representations automatically from the underlying spatiotemporal data. Effective function approximation capability: If Deep Learning models have enough layers and neurons, they can fit any curve and approximate any complex non-linear function. Perform better with big data: Conventional machine learning techniques, such as Support Vector Machines and decision trees, typically outperform big data alternatives on smaller datasets before reaching a plateau. When more data is added, deep learning models' performance may continue to improve. Types of Spatio-Temporal Data were identified by Wang et al. in 2022: 1. Event Data: Discrete events that occur in specific locations at specific times make up event data. An event's type, place of occurrence, and time of occurrence can all be used to define it in general terms. 2. Trajectory Data: Typically, location sensors installed on moving objects provide trajectory data. The sensors transmit and record the object's locations over time at regular intervals. 3. Point Reference Data: A collection of moving reference points scattered over a specific area and time period is used to create these measurements. 4. Video Data: A video (sequence of images) can be categorized as spatio-temporal data type. Neighboring pixels typically share similar RGB values in the spatial domain, exhibiting strong spatial correlations. Succeeding frames exhibit significant temporal dependence in the temporal domain. The highly complex, substantial, and quickly expanding Spatio-Temporal data, however, continue to present problems. Some of the challenges include the development of interpretable models, and fusion of multi-modal spatio- temporal datasets[1]. We categorize surveyed research papers with respect to their domain, such as Transportation, Environment etc. For each category, we put forth the problem solved by the referenced literature, it’s methodology, and results in a concise manner. We have followed a thematic and chronological method for presenting our survey. 2. LITERATURE REVIEW 2.1 Transportation Zhou et al., 2022 focuses on the challenge of modelling the intrinsic correlation of the ST features that were extracted by the convolutional network apart from the ST aggregation in traffic data, as predictions made as a result of this may have biases that affect subsequent transportation planning