The energy transition is increasing pressure on utility infrastructure projects to be delivered efficiently and at scale. At the same time, integrated delivery approaches are transferring greater design responsibility to contractors, creating opportunities to incorporate construction knowledge earlier in the design process. However, constructability assessments remain largely informal and experience-based, leading to inconsistent evaluations and a risk of overlooking construction challenges. This paper investigates how geospatial technologies can support a more systematic assessment of constructability during the engineering stage of low-voltage utility network projects. Using a Design Science Research approach, a GIS-based Decision Support System (DSS) was developed that formalises constructability knowledge into explicit spatial assessment rules. The prototype combines urban spatial data and project-specific design information to assess route feasibility, workspace requirements, and construction-method constraints. Through rule-based spatial analysis, the system identifies hard, soft, and workflow clashes between proposed cable route designs and their surrounding environment. Demonstration on a real-world project and evaluation with industry practitioners indicate that the approach supports more consistent and transparent identification of constructability issues. The results demonstrate the potential of GIS-based decision support to bridge the gap between experience-based engineering practice and data-driven constructability assessment.
Neighbourhood sustainability planning needs tools that integrate heterogeneous urban datasets, expose modelling assumptions, and allow transparent comparison of intervention strategies across domains. Therefore, this paper presents a neighbourhood-scale urban digital twin (UDT) prototype for Twekkelerveld, Enschede, combining energy and ecological indicators in a single interactive environment. The co-developed prototype links building-level energy demand, rooftop photovoltaic potential, vegetation and tree-based ecosystem indicators, and satellite-derived urban heat information within a shared GIS-based workflow. Its main contribution is a reproducible integration pipeline that harmonises geospatial, statistical, ecological, and remote-sensing data through explicit, editable scenario-response functions. Technical validation confirmed consistent scenario behaviour. Regression-based explanatory analysis of 800 Monte Carlo runs showed that PV economic outcomes depend mainly on PV unit cost and yield assumptions, while tree annualised cost is driven by intervention cost, discounting, and time horizon. Multi-criteria decision analysis (MCDA) rankings stayed highly stable under 20% weight variation (median Spearman’s ρ ≈ 0.998), and stakeholder feedback confirmed practical usefulness for neighbourhood planning. The results show that neighbourhood-scale digital twins can support urban informatics and smart-city decision-making by making trade-offs among retrofitting, photovoltaics, greening, heat mitigation, and spatial equity visible in practice.
Data integration is a significant technical challenge that hinders the full implementation of Urban Digital Twins (UDTs). Given their data-driven nature, UDTs rely on the integration of heterogeneous data sources, schemas, and structures. This process is inherently complex due to the diversity of techniques and systems involved. Furthermore, with the emergence of UDTs, the focus has shifted from acquiring new data through sensors to exploring how existing data can be integrated, restructured, and connected to meet the evolving requirements of digital twins. In this context, we raise fundamental questions about data integration within UDTs. Building on a previously proposed conceptual framework defining levels of data integration, this study gathers insights from practitioners actively engaged in UDT implementations. The aim is to understand how these conceptual integration levels are applied in practice, how they are selected across the UDT lifecycle, what tools support their implementation, and how they enable effective integration depending on the availability and nature of the data. By aligning theoretical frameworks with practical implementations, this research aims to enhance the effectiveness and adoption of scalable and flexible data integration strategies in the development, use, and maintenance of UDTs. The findings highlight the relevance of data integration levels for UDT practitioners, demonstrating that they were able to identify which levels are involved in their projects. Additionally, the results provide insights into how each level of integration is commonly applied across different stages of the lifecycle, as well as the tools and methods associated with each level.
Urban air quality monitoring using satellite remote sensing requires consistent, spatially comparable datasets across multiple pollutants. This paper presents an end-to-end, open-source GIS-based methodology for processing Sentinel-5P TROPOMI Level-2 products to generate synchronized multi-pollutant datasets suitable for urban-scale analysis. The workflow integrates automated data acquisition via the Copernicus Data Space Ecosystem OData API, quality-controlled preprocessing using the HARP toolbox and a QGIS-based spatial analysis pipeline developed in Python. The main methodological contribution is а spatial intersections masking approach, in which a daily mask of valid pixels is calculated as a logical intersection of valid observations for carbon monoxide (CO), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂). This ensures that all statistical comparisons between pollutants are derived from spatially coinciding sets of pixels, thereby eliminating location-based sampling biases arising from differences in swath geometry, cloud cover, and gas-specific retrieval sensitivity. The methodology was applied to four consecutive winter seasons (2019–2023) over Sofia, Bulgaria, a city with significant air pollution driven by residential solid-fuel combustion. The resulting synchronized daily database enables temporal trend analysis (Mann-Kendall test), inter-pollutant correlation assessment, and pollution hotspot identification. The workflow relies entirely on open-source software (QGIS, GDAL/OGR, NumPy, HARP) and is designed to be easily adaptable to any urban area and any combination of TROPOMI gas products with minimal parameter adjustments.
This study proposes a data-driven method for evaluating and optimizing office layouts based on worker behavioral monitoring data obtained from Bluetooth Low Energy (BLE) beacons. First, a worker behavior model considering office floor plans and furniture layouts is developed to refine location data obtained from beacons through correction and interpolation processes. Using the refined data, worker activities such as spatial movements, seating states, and interpersonal interactions are extracted. The accuracy of the activity extraction method is verified through observational surveys conducted in an actual office environment. Next, the spatiotemporal distributions of worker activities are analyzed using kernel density estimation to reveal differences in activity patterns depending on worker attributes and time periods. Based on these activity data, an office layout evaluation index is defined using indicators representing inter-department collaboration intensity and shared-space usage frequency. Finally, a genetic algorithm is applied to search for office layouts that maximize the proposed evaluation index. The results demonstrate that the proposed framework enables the quantitative evaluation and generation of efficient office layouts based on empirical behavioral data, thereby providing a practical approach for supporting data-driven workplace planning in smart office environments.
