Despite the growing interest in digital twins (DTs) in geospatial technology, the scientific literature is still at the early stage, and concepts of DTs vary. In common perspectives, the primary goals of DTs are to reduce the uncertainty of the physical systems in real-world projects to reduce cost. Thus, this study is aimed at developing a structural schematic of a geographic information system (GIS)-enabled DT system and exploring geospatial technologies that can aid in deploying a DT system for a real-world project—in particular, for the sustainable evaluation of carbon emissions. The schematic includes three major phases: (1) data collection and visualization, (2) analytics, and (3) deployment. Three steps are designed to propose an optimal strategy to reduce carbon emissions in an urban area. In the analytics phase, mapping, machine learning algorithms, and spatial statistics are applied, mapping an ideal counterpart to physical assets. Furthermore, not only are GIS maps able to analyze geographic data that represent the counterparts of physical assets but can also display and analyze spatial relationships between physical assets. In the first step of the analytics phase, a GIS map spatially represented the most vulnerable area based on the values of carbon emissions computed according to the Intergovernmental Panel on Climate Change (IPCC) guidelines. Next, the radial basis function (RBF) kernel algorithm, a machine learning technique, was used to forecast spatial trends of carbon emissions. A backpropagation neural network (BPNN) was used to quantitatively determine which factor was the most influential among the four data sources: electricity, city gas, household waste, and vehicle. Then, a hot spot analysis was used to assess where high values of carbon emissions clustered in the study area. This study on the development of DTs contributes the following. First, with DTs, sustainable urban management systems will be improved and new insights developed more publicly. Ultimately, such improvements can reduce the failures of projects associated with urban planning and management. Second, the structural schematic proposed here is a data-driven approach; consequently, its outputs are more reliable and feasible. Ultimately, innovative approaches become available and services are transformed. Consequently, urban planners or policy makers can apply the system to scenario-based approaches.

The creation of augmented reality-related geographic information system (GIS) mapping applications has witnessed considerable advances in the technology of urban modeling; however, there are limitations to the technology that is currently used to create similar resources. The cost of the creation of the vehicle is an obstacle, and the rendering of textures of buildings is often lacking because of the distortion caused by the types of lenses that have been used. Generally, mobile mapping systems (MMSs) can extract detailed three-dimensional (3D) data with high quality texture information of the 3D building model. However, mapping urban areas by MMSs is expensive and requires advanced mathematical approaches with complicated steps. In particular, commercial MMS, which generally includes two GPS receivers, is an expensive device, costing ~$1 million. Thus, this research is aimed at developing a new MMS that semi-automatically produces high-quality texture information of 3D building models proposes a 3D urban model by hybrid approaches. Eventually, this study can support urban planners and people to improve their spatial perception and awareness of urban area for Smart City Planning.

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This study explored geospatial technologies to support efforts of community organizations and of the Chicago Gun Violence Research Collaboration to reduce gun-related crimes. It entailed (1) identification of spatial trends in gun-related crimes during 2012 to 2017 in each of the following four areas: Austin, East Garfield, North Lawndale, and Englewood; (2) investigation of changes in crime patterns near safe school zones in the areas before and after the establishment of the city’s safe passage routes in 2009 to protect the youth from street violence when traveling to school; and (3) development of a web-enabled mobile application to provide researchers and residents with spatial information on local crime incidents and to enable community members to collect and share their information in a GIS environment. The results of this research revealed that, although the number of safe passage routes has increased in these areas over the past several years, hotspot trends for gun-related crimes have intensified in most of the communities in these areas, which include school zones and safe passage routes. Accordingly, it turned out that GIS can serve as an ideal platform supporting collaborative efforts between communities and researchers.

This paper develops a comprehensive assessment of post-disaster housing and tourism resource recovery. It enables us to address how many natural and man-made features in a tourist town have recovered after a hurricane event. The assessment uses a variety of sources, at different spatial scales and at different points in time. Furthermore, this study develops a measurement scale to quantify damage and recovery appropriate for the available resources. In particular, the study focuses on the development of a methodological approach to tracking housing and tourism resource recovery and helping local communities recover faster the damaged areas after disaster. The effort uses multiple sources of data, including questionnaire data, Federal Emergency Management Agency (FEMA) damage data, airborne light detection and ranging (LiDAR) data, and remote sensing satellite images. The data are quantitatively analyzed to fulfill the objectives of assessing housing recovery rate over time and are represented on maps. The maps are used to represent the status of damaged buildings (e.g., no damage, minor or major damage, affected or destroyed). Furthermore, repaired buildings in specified time intervals are represented on the maps. Eventually, this study develops two schematic diagrams illustrating the average damage and the weighed recovery from multiple data sources. The outcomes of this study will help decision makers emphasize on the locations identified as experiencing differential progress in the reconstruction, rebuilding, and repairing of houses or tourism resources.