Location Intelligence Research in System Sciences

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https://hdl.handle.net/10125/109981

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    Integration of Two Types of School Districts
    (2025-01-07) Shimizu, Hitoshi; Okabe, Yasunori; Suwa, Hirohiko; Yasumoto, Keiichi
    In Japan, compulsory education consists of six years in elementary school and three years in junior high school. But recently, the government has pushed to introduce integrated schools for nine years that combine the functions of elementary and junior high schools. Becuase the current elementary and junior high school districts are so intricate that it is not easy to determine which schools should be converted to integrated schools. Moreover, to make it easier for residents to agree, school districts similar to both elementary and junior high school districts should be created for integrated schools. Therefore, we first propose to use Adjusted Rand Index (ARI) as a measure of similarity of two partitions of districts. Then, we formulate the problem of deciding which schools should be converted to integrated elementary and junior high schools as a linear programming problem and propose a procedure for creating a school district reorganization plan. In a simulation experiment using the Nara City dataset, we confirm that the proposed method creates school districts for integrated schools that are similar to those of existing elementary and junior high schools.
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    A Geodemographic Analysis of Social Media Access in U.S. Counties
    (2025-01-07) Sarkar, Avijit; Pick, James
    This paper analyzes geographic patterns and geodemographic influences on social media access in 3,076 counties in the lower-48 states of the United States. Disparities in access of five social media and networking platforms – Facebook, Twitter, LinkedIn, Instagram, and YouTube are revealed by mapping, and hotspots and coldspots are analyzed using geostatistical methods. Clusters of counties with various levels of social media access are characterized in terms of their geodemographic attributes showing similarities and differences across such clusters. Regression findings reveal that differences in social media access in U.S. counties are associated with extent of urbanization, race/ethnic distribution of African American and Hispanic populations, young dependency ratio, working age population, and types of occupations, namely professional, scientific, and technical services and service sector occupations. Implications of these findings are discussed within the context of the prevailing digital divide in the United States.
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    Towards an Approach on Location Data Analysis for Reusable Small-load Carriers
    (2025-01-07) Müller, Jonas; Eberhardt, Lars; Wahyudi, Vincent; Storath, Martin; Dobhan, Alexander
    This paper describes a new approach to collect and extrapolate location data for small load carriers (SLCs) as a basis for the simulation of multi-site SLC cycles. The approach enables realistic multi-site SLC cycle simulation studies, which can lead to better de-cisions on cycle parameters, which in turn leads to cost reductions and makes the use of reusable SLCs more attractive. It is based on 3 sub-studies with dif-ferent location technologies to test them for use in SLC cycles. We compare the technologies concerning real-world requirements and use the results of two of these sub-studies to develop our approach for ex-trapolating data. According to our research, such an approach has not yet been applied to multi-site SLC cycles. Our research contributes to SLC management research and location intelligence research.
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    Deepot: Parking Lot Identification Using Low-Resolution Satellite Imagery
    (2025-01-07) Ding, Xianzhong; Hong, Wanshi; An, Zhiyu; Wang, Bin; Du, Wan
    Detecting parking lots is essential for siting electric truck charging infrastructure, ensuring convenient access for drivers. However, the private ownership of many parking lots limits public access to information for planners. Current object detection methods rely heavily on high-resolution satellite imagery, which is often restricted in availability. To address this, we introduce Deepot, a deep learning-based truck parking location identification approach using low-resolution satellite imagery. We begin by retrieving geographical data for the target area and optimizing image resolution for model training. The model produces initial parking lot locations, which are then converted to real global latitude and longitude coordinates using a custom coordinate reference systems transformer. These global coordinates facilitate querying Google Maps for high-resolution images to enhance detection performance. Deepot streamlines charging infrastructure planning from diverse satellite imagery and offers additional, high-fidelity candidate locations to medium- and heavy-duty infrastructure planning tool, HEVI-LOAD. Extensive experiments show the effectiveness of Deepot.
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    Developing Indicators and Forecasting Demand for Enhanced Regional Community-Based Integrated Care Systems in Japan
    (2025-01-07) Fujita, Kaede; Ichikawa, Manabu
    This study evaluates the current state and future demand of the Community-based Integrated Care System (CICS) across Japan’s 1889 municipalities. Aimed at achieving a better CICS, this research developed indicators to evaluate the existing care structures within each municipality. Utilizing 73 indicators covering housing, medical care, long-term care, livelihood support, and preventive services, we conducted a comprehensive analysis to identify regional disparities and strengths. The indicators were visualized using radar charts and standardized to enable cross-regional comparisons. Future projections were made to estimate service and personnel demands, highlighting significant regional variations and emphasizing the need for region-specific policy planning and resource allocation. Our system allows for the evaluation and comparison of policies based on these indicators, determining their long-term significance. This research provides critical insights for policymakers and healthcare providers, offering a solid foundation for enhancing the CICS framework and supporting the creation of resilient, elderly-friendly communities throughout Japan.
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    Interpolating Socioeconomic Data for Crime Regression Models at the Neighborhood Level
    (2025-01-07) Snowden, Aleksandra; Stepnowski, Kendra
    Prior studies of spatial crime patterns often turn to the U.S. Census administrative units as proxies for neighborhoods. We know less about the feasibility of using areal interpolation methods to help estimate neighborhood-level regression models. We used ArcGIS Pro spatial interpolation methods to create neighborhood estimates of socioeconomic characteristics based on the Census data. Then, we examined the relationship between neighborhood characteristics, including racial heterogeneity, residential mobility, immigration, and concentrated disadvantage, and crime at several levels of analysis. We demonstrated that the spatial interpolation methods can offer reliable and accurate estimates of neighborhood measures when socioeconomic data are only available at the Census block group and tract levels. Moreover, we found a significant association between concentrated disadvantage and crime at all levels of analysis. This study has important implications for social and systems science research with focus on area characteristics to predict spatially distributed outcomes, including crime.