Applying any sustainable intervention in the urban energy system requires fundamental knowledge of the energy demand dynamics. Only when we can predict the users' energy demand at any given time with accuracy, we can redesign the urban energy system. Accordingly, the main objective of this paper is to determine the annual electricity usage of the building connections in the urban built environment. In this paper firstly through a literature review, the important electricity usage explanatory variables of the built environment are recognized. For each building, besides the annual electricity usage, three major categories of explanatory variables, including physical, socioeconomic, and geospatial characteristics are determined. Based on the available data sources, a building electricity usage database is created. The database is categorized based on the two most frequently used building sectors including residential and non-residential. Ordinary Least Squares (OLS) technique is applied to the constructed database to estimate the predicting model parameters establishing a relationship between the annual electricity usage as a dependent variable and physical, socioeconomic, and geospatial variables as independent variables. In this research, to determine the contribution of geospatial characteristics in the annual electricity usage variability, regression analysis is performed in two consecutive steps. In the first step only, the geospatial characteristics were implemented in the multiple linear regression analysis. Following that, in the second step, the other categories including physical and socioeconomic characteristics are added to the model. The result revealed that in both building sectors most of the predictors are statistically significant at the 0.05 level. While for the residential buildings the geospatial characteristics account for 9.7% of the electricity usage variation, these values for the service and industry sub-sectors are 9.9% and 8.7% respectively. In total, all variables explain 28.1%, 39.4%, and 42.9% of the electricity usage variability of residential, service, and industrial buildings respectively.

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Industrial land is under transition globally. Insights into this transition are important to plan a sustainable future. Since industrial land follows parcel shapes and the transition process requires multi-year data to observe the impacts of such changes, multi-year vector data should be used to analyse industrial land transition. This paper presents a framework to analyse path-dependent regional industrial land transition processes using vector data. A step by step instruction is presented. In the analysis, the changed percentages of land use in the surroundings of appeared or disappeared industrial land are visualized. The visualized surrounding land use compositions give planners an idea on what causes land use transitions, the most frequent transition forms and their impacts on the surroundings, purely from a land use point of view to reduce data collection efforts. The North Brabant region in the Netherlands is used as a case study. The region is split into urban and non-urban areas to show the generic applicability of this framework.

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Abandoned industrial sites could be redeveloped in a sustainable way with the help of previous experience. This paper presents a case-based reasoning (CBR) approach to support sustainable industrial site redevelopment. For a target site that needs to be redeveloped, qualitative important key concerns are identified and quantitative attributes, which are important for sustainability, are calculated. The key concerns are generated from zoning documents and the attributes are calculated from spatial data sets. Machine learning techniques are used to find the most influential attributes to determine transition forms. Similar cases from the constructed case base are retrieved based on the algorithm the authors have proposed. The North Brabant region in the Netherlands is used as a case study. A web application is presented to illustrate the approach. The e-planning method provides a straightforward way to retrieve transition forms from similarly redeveloped cases for new regional planning tasks with a focus on sustainability.@en

Lingering vacant facilities deteriorate the condition of an urban environment, and, as a consequence, actuate neighboring companies to leave the area as well. In addition, new development efforts keep depleting scarce land resources. In this paper, a framework is presented to match existing vacant facilities to the requirements of potential customers or owners to promote sustainable redevelopment and reuse. Important attributes for facility reuse are identified from literature. To automatically extract these attributes from models and their surroundings, Geospatial information and Building Information Models (BIM) are combined. In the proposed framework, a profile is created for each existing vacant facility by combining BIM and GIS attributes. As a result, these profiles can be matched to the desired BIM model, which the aspiring users have provided, based on a weighted distance calculation. The framework presents the most suitable vacant facilities to the users in order to promote facility reuse. These facility reuse alternatives are evaluated based on a single monetary metric that represents the effort required to partially or fully accommodate the requirements of the aspiring users, which is reflected in the weighted distance between profiles from existing vacant facilities and the facility desired by the end-user. This framework identifies suitable areas for redevelopment after which a process is started that forms an iterative and comprehensive evaluation dialog between demand and supply parties, on multiple scale levels, including various design alternatives to adapt the existing facility to the desires of the consumer and revitalize the surroundings according to Geodesign principles. A proof-of-concept of the framework is presented together with the conceptual system structure. Evaluation of the attributes and the technical implementation of their extraction from BIM and GIS data show the technical feasibility of the approach.@en