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Understanding anomalous behavior and spatial changes in an urban parking area can enhance decision-making and situational awareness insights for sustainable urban parking management. Decision-making relies on data that comes in overwhelming velocity and volume, that one cannot comprehend without some layer of analysis and visualization. This work presents a mobile application that performs time series analysis and anomaly detection on parking lot data for decision-making. The mobile application allows users to add pins in the parking lot and analyze the pin data over a period of time. Our approach uses parking pins to identify each vehicle and then collect specific data, such as temporal variables like latitude, longitude, time, date, and text (information from the license plate), as well as images and videos shot at the location. Users have the option of placing pins at the location where their car is parked, and the information collected can be used for time series analysis. By examining the data pattern, we may quickly identify vehicles parked in restricted spaces but without authorization and vehicles parked in disabled spaces but owned by regular users. This time series analysis enables the extraction of meaningful insights, making it useful in the identification of recurring patterns in parking lot occupancy over time. This information aids in predicting future demands, enabling parking administrators to allocate resources efficiently during peak hours and optimize space usage. It can be used in detecting irregularities in parking patterns, aiding in the prompt identification of unauthorized or abnormal parking and parking violations which includes parking of the wrong type of vehicle, and parking at restricted or reserved areas. 

Crime data visualization plays a key role in understanding and dealing with criminal activities. This paper focuses on the integration of mixed reality (MR) and crime data analysis. There are many barriers and challenges when developing MR three-dimensional (3D) environments for visualization and inspection. The main problem is the lack of commonly shared data structures and interfaces between them. The rise in crime rates over the past few years is a huge source of issue for police departments and law enforcement organizations. As the crime rates significantly changed throughout time, both upward and downward, these changes are then compared to external factors, such as population, unemployment, and poverty. There is a need for visualizing the multiple crime datasets in multiple states with external factors. This work proposes a novel interactive approach for loading crime datasets into the HoloLens 2 device and displaying them in a mixed-reality setting for data analysis. By allowing people to engage and analyze datasets in a 3D space, the suggested system seeks to close the gap between data analysis and machine learning. Users can import many datasets, such as spatial, category, and numerical data, into the HoloLens 2 device and interactively visualize crime data for different states simultaneously. The system offers user-friendly capabilities for interactive data visualization in mixed reality once the data has been imported. The dataset is manipulated and transformed by users, who can also rotate, scale, and position it in 3D. To depict various characteristics and dimensions of the data, the system also supports a variety of visual encoding techniques, such as color mapping, size scaling, and spatial layout with the use of the imported datasets and the HoloLens 2’s visualization capabilities, users can discover new insights and intricate linkages within the data. Natural movements and voice instructions allow users to engage with the visible data, enabling a hands-free and immersive data exploration experience. This paper also visualizes the crime data for four different cities: Chicago, Baltimore, Dallas, and Denton. Analyzing crime against factors such as population, employment, unemployment rate, and poverty rates provides information about the complex relationship between social factors and criminal behavior. The results and outcomes of this work will help the police department and law enforcement organizations better understand crime issues and supply insight into factors affecting crime that will help them deploy resources and help their decision-making process. 

The rise in crime rates over the past few years is a major issue and is a huge source of worry for police departments and law enforcement organizations. Crime severely harms the lives of victims and the communities they live in many places throughout the world. It is an issue of public disturbance, and large cities often see criminal activity. Many studies, media, and websites include statistics on crime and it is contributing elements, such as population, unemployment, and poverty rate. This paper compares and visualizes the crime data for four different cities in the USA, namely Chicago, Baltimore, Dallas, and Denton. We assess areas that are significantly affected based on zip codes and variations in crime categories. As the crime rates have significantly changed both upward and downward throughout time, these changes are compared to their external causes such as population, unemployment, and poverty. The results show crime frequency and distribution across four different cities and supply valuable information about the complex relationship between social factors and criminal behavior. These results and outcomes will help the police department and law enforcement organizations better understand crime issues, map crime incidents onto a geographical map, and supply insight into factors affecting crime that will help them deploy resources and help in their decision-making process.