Location Intelligence Research in System Sciences
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ItemUsing Geolocated Text to Quantify Location in Real Estate Appraisal( 2022-01-04)Accurate real estate appraisal is essential in decision making processes of financial institutions, governments, and trending real estate platforms like Zillow. One of the most important factors of a property’s value is its location. However, creating accurate quantifications of location remains a challenge. While traditional approaches rely on Geographical Information Systems (GIS), recently unstructured data in form of images was incorporated in the appraisal process, but text data remains an untapped reservoir. Our study shows that using text data in form of geolocated Wikipedia articles can increase predictive performance over traditional GIS-based methods by 8.2% in spatial out-of-sample validation. A framework to automatically extract geographically weighted vector representations for text is established and used alongside traditional structural housing features to make predictions and to uncover local patterns on sale price for real estate transactions between 2015 and 2020 in Allegheny County, Pennsylvania.
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ItemLocation intelligence system for people estimation in indoor environment during emergency operation( 2022-01-04)In the last years, location intelligence systems have been characterized by an increasing interest in several sectors. Among them, those of emergencies are mainly involved in order to enhance the rescue procedures and to reduce the intervention time, especially within indoor environment where GPS does not support the emergency operations. The authors define a low cost location intelligence system based on Channel State Information (CSI) of Wi-Fi and low-energy ESP32 SoC platform to analyze CSI data of Wi-Fi Signals. The technical solution utilizes wavelet filter to remove background noise in the CSI data, Principal component analysis (PCA) to reduce the dimensionality of the CSI data and get the most valuable data that are used as feature for the defined DNN model. The experimental results show the best performance of this model compared to the other machine learning (ML) algorithms analysed.
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ItemLocation Analytics for Transitioning to Fire Resilient Landscapes( 2022-01-04)Wildfire risk is significant for forest and vegetative landscapes, particularly in regions where climate change is resulting in prolonged droughts and extended fire seasons that are a fire risk to people and property. An important component of mitigation is restoration programs that transition landscapes to be more fire resilient. A collaborative partnership between the US Forest Service and university researchers is reported that takes advantage of location intelligence. This paper reviews this general planning problem and details location analytic based approaches for informing mitigation efforts. Application of results highlight the ability to optimize goals and objectives while maintaining project area needs and treatment thresholds.
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ItemGeospatial Clustering Analysis on Drug Abuse Emergencies( 2022-01-04)The epidemic of drug abuse is a serious public health issue in the U.S. The number of overdose deaths involving prescription opioids and illicit drugs has continuously increased over the last few years. This study aims to develop a geospatial model that identifies geospatial clusters in terms of socioeconomic and demographic characteristics with an unsupervised machine learning algorithm. Then, we suggest the most important features affecting heroin overdose both negatively and positively. The findings of this study may inform policymakers about strategies to mitigate the drug overdose crisis.
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ItemEvacuation Shelter Scheduling Problem( 2022-01-04)Evacuation shelters, which are urgently required during natural disasters, are designed to minimize the burden of evacuation on human survivors. However, the larger the scale of the disaster, the more costly it becomes to operate shelters. When the number of evacuees decreases, the operation costs can be reduced by moving the remaining evacuees to other shelters and closing shelters as quickly as possible. On the other hand, relocation between shelters imposes a huge emotional burden on evacuees. In this study, we formulate the ``Evacuation Shelter Scheduling Problem,'' which allocates evacuees to shelters in such a way to minimize the movement costs of the evacuees and the operation costs of the shelters. Since it is difficult to solve this quadratic programming problem directly, we show its transformation into a 0-1 integer programming problem. In addition, such a formulation struggles to calculate the burden of relocating them from historical data because no payments are actually made. To solve this issue, we propose a method that estimates movement costs based on the numbers of evacuees and shelters during an actual disaster. Simulation experiments with records from the Kobe earthquake (Great Hanshin-Awaji Earthquake) showed that our proposed method reduced operation costs by 33.7 million dollars: 32%.
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ItemAssessing Multi-Agent Reinforcement Learning Algorithms for Autonomous Sensor Resource Management( 2022-01-04)Unmanned aerial vehicles (UAVs) have applications in search and rescue operations and such operations could be more efficient by using appropriate artificial intelligence (AI) to enable a UAV agent to operate autonomously. Sensor resource management (SRM), which leverages capabilities across location intelligence, facilitates the efficient and effective use of UAVs and their sensors to complete a set of tasks. Furthermore, multiple UAVs, each with different sensor configurations, must be considered when maximizing mission effects. Instantiating operational autonomy for such teams requires considerable coordination. One AI approach relevant to this task is multi-agent reinforcement learning (MARL). However, MARL has seen limited prior use in SRM. This work evaluates the trade-space of MARL algorithms with respect to performing heterogeneous sensor resource management (SRM) tasks, considers the concept of evaluating MARL in a test and evaluation framework and compares a suit of algorithms with random and Bayesian hyperparameter optimization methods.
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ItemA Method for the Optimized Placement of Bus Stops Based on Voronoi Diagrams( 2022-01-04)In this paper a new method for placing bus stops is presented. The method is suitable for permanently installed new bus stops and temporarily chosen collection points for call busses as well. Moreover, our implementation of the Voronoi algorithm chooses new locations for bus stops in such a way that more bus stops are set in densely populated areas and less in less populated areas. To achieve this goal, a corresponding weighting is applied to each possible placement point, based on the number of inhabitants around this point and the points of interest, such as medical centers and department stores around this point. Using the area of Roding, a small town in Bavaria, for a case study, we show that our method is especially suitable for for rural areas, where there are few multi-family houses or apartment blocks and the area is not densely populated.
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