Deliverable 6.7 describing the GeoViz application for GeoData visualisation, developed for the EU-funded project ILIAD.
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Best practice guide on creating an IT architecture that supports smart mobility services. Joint work of Karlstad University and Hanze University of Applied Sciences within the Interrg IVb project ITRACT.
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and meteorological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be Gated Recurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and metereological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be GatedRecurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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Purpose: The purpose of this study is to assess the evolution of restaurant locations in the city of Hamilton over a 12-year period (1996 to 2008) using GIS techniques. Retail theories such as central place, spatial interaction and principle of minimum differentiation are applied to the restaurant setting. Design/methodology/approach: A database of restaurants was compiled using the NZ yellow pages and contained 981 entries that consisted mainly of location addresses and types of cuisine. This paper focuses on locational patterns only. Findings: A process of geo-coding and clustering enabled the identification of two clustering periods over 12 years for city restaurants, indicating locational patterns of agglomeration within a short walking distance of the CBD and spill over effects to the north of the city. Research limitations/implications: The data do not allow statistical analysis of the variables causing the clustering but offer a visual description of the evolution. Explanations are offered on the possible planning regimes, retail provision and population changes that may explain this evolution. Practical implications: The findings allow identification of land use patterns in Hamilton city and potential areas where new restaurants could be developed. Also, the usefulness of geo-coded data in identifying clustering effects is highlighted. Originality/value: Existing location studies relate mostly to site selection criteria in the retailing industry while few have considered the evolution of restaurant locations in a specific geographic area. This paper offers a case study of Hamilton city and highlights the usefulness of GIS techniques in understanding locational patterns.
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Geospatial technologies have the potential to transform the lives of older adults by providing them with necessary tools to navigate their local communities, access services, connect with others, and access valuable information. However, the usability and accessibility of such technologies often fall short of the needs of older adults. Many existing geospatial tools are not designed with the needs and preferences of older adults in mind; this can lead to usability challenges and limit their usage. This paper explores a participatory approach in developing an inclusive geodata-collection tool that is specifically tailored to older users’ needs. The paper also highlights the importance of incorporating user-centered design principles, participatory design methods, and accessibility guidelines throughout the entire geodata-tool-development process. It also emphasizes the need for ongoing user engagement and feedback in order to ensure that the tool remains relevant and usable in the evolving digital landscape. This participatory approach has resulted in a tool that is easy to use and accessible for older adults; it is available in various languages, thus ensuring that the elderly can actively participate in the prototype’s creation and contribute to the collection of the geospatial information that reflects their lived experiences and needs.
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The authors present the design of the shipping simulation SEL and its integration in the MSP Challenge Simulation Platform. This platform is designed to give policymakers and planners insight into the complexity of Maritime Spatial Planning (MSP) and can be used for interactive planning support. It uses advanced game technology to link real geo- and marine data with simulations for ecology, energy and shipping. The shipping sector is an important economic sector with influential stakeholders. SEL calculates the (future) impact of MSP decisions on shipping routes. This is dynamically shown in key performance indicators (e.g. route efficiencies) and visualised in heat maps of ship traffic. SEL uses a heuristic-based graph-searching algorithm to find paths from one port to another during each simulated month. The performance of SEL was tested for three sea basins: the firth of Clyde, Scotland (smallest), North Sea (with limited data) and Baltic Sea regions (largest, with most complete data). The behaviour of the model is stable and valid. SEL takes between 4 and 17 seconds to generate the desired monthly output. Experiences in 20 sessions with 302 planners, stakeholders and students indicate that SEL is a valuable addition to MSP Challenge, and thereby to MSP.
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There are a plethora of drivers of change in energy systems until 2015. The role of social and political actors is likely to be more noticeable. In Europe, locally, high-impact ideas like green consumerism and limited acceptance of energy systems that result in trade-offs will be important. Nationally, the empowerment of individuals and communities and the politicization of energy-related issues will be drivers of change. Internationally, energy issues will become more important in the foreign and security policies of state and non-state actors.
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