From systems to patterns and back - Exploring the spatial role of dynamic time and direction patterns in the area of regional planning

This master thesis presents a data-driven framework to explore the role of dynamic time and direction patterns in the area of Finnish Lapland in order to improve decision-making in urban planning and design tasks. The Arctic Ocean Railway project is chosen as a case study. In an era marked by dramatic environmental, political and societal changes, the Arctic region becomes more global and complex. An increasing number of actors are involved in its spatial transformations. Due to melting ice, the Northern Sea Route gains attention from the shipping and trade industries that are manifested in new port and infrastructure projects. Eco-tourism is booming in the Arctic due to its imaginary remoteness, while local Indigenous People try to preserve traditional livelihoods. In order to cope with the increasing complexity of such dynamic urban and regional challenges, Systems Thinking, dynamic patterns, modelling and use of simulation are researched to open up novel ways for complex regional planning methods. This is achieved by designing an agent-based model and using different representation and abstraction features for different dynamic data packages. The project is integrated within the GAMA simulation platform (a modelling and simulation development environment for building spatially explicit agent-based simulations) and embedded in the MIT CityScope framework - a medium for both, analyzing agent’s behavioural patterns and displaying them to the relevant stakeholders. The project attempts to address the necessity to handle the increasing complexity by presenting a dynamic, evidence-based planning and decision support tool called CityScope Lapland. The main goal of CityScope Lapland is to use digital technologies to incorporate variables like time and direction in urban spatial analysis and methodology; secondly, to improve the accessibility of the decision-making process for non-experts through a tangible user interface, and third, to help users evaluate their decisions by creating a feedback through real-time visualization of urban simulation results when facing less and less predictable futures. The project provides an alternative design approach, introducing new forms of urban imagination and different ways of perceiving and measuring complex spatial transformations

From Systems to Patterns and Back - Exploring the spatial potential of dynamic patterns in the area of regional planning

The main goal of this paper is to present a decision support tool that translates systemic thinking and dynamic patterns into an immersive computational design method and through improved communication and simulation of abstract and complex urban data enhances the planning processes dialogue between different stakeholders and supports decision-making processes. The author presents a multi-level immersive and tangible interface setup consisting of technical and conceptual elements that, as a whole, through the use of dynamic patterns visualise the interaction of distinctive agents in the Finnish Lapland. It addresses the lack of a holistic approach and incorporation of dynamic patterns in the planning process by proposing a decision support tool that uses the results from these investigations to inform decision-making in planning and design tasks.Peer reviewe

Automated Semantic SWOT Analysis for City Planning Targets: Data-driven Solar Energy Potential Evaluations for Building Plots in Singapore

Singapore’s urban planning and management is cross-domain in nature and need to be assessed using multi-domain indicators — such as SDGs. However, urban planning processes are often confronted with data interoperability issues. In this paper, we demonstrate how a Semantic Web Technology-based approach combined with a SWOT analysis framework can be used to develop an architecture for automated multi-domain evaluations of SDG-related planning targets. This paper describes an automated process of storing heterogeneous data in a semantic data store, deriving planning metrics and integrating a SWOT framework for the multi-domain evaluation of on-site solar energy potential across plots in Singapore. Our goal is to form the basis for a more comprehensive planning support tool that is based on a reciprocal relationship between innovations in SWT and a versatile SWOT framework. The presented approach has many potential applications beyond the presented energy potential evaluation

Semantic 3D City Agents—An intelligent automation for dynamic geospatial knowledge graphs

This paper presents a system of autonomous intelligent software agents, based on a cognitive architecture, capable of automated instantiation, visualisation and analysis of multifaceted City Information Models in dynamic geospatial knowledge graphs. Design of JPS Agent Framework and Routed Knowledge Graph Access components was required in order to provide backbone infrastructure for an intelligent agent system as well as technology agnostic knowledge graph access enabling automation of multi-domain data interoperability. Development of CityImportAgent, CityExportAgent and DistanceAgent showcased intelligent automation capabilities of the Cities Knowledge Graph. The agents successfully created a semantic model of Berlin in LOD 2, compliant with CityGML 2.0 standard and consisting of 419 909 661 triples described using OntoCityGML. The system of agents also visualised and analysed the model by autonomously tracking interactions with a web interface as well as enriched the model by adding new information to the knowledge graph. This way it was possible to design a geospatial information system able to meet demands imposed by the Industry 4.0 and link it with the other multi-domain knowledge representations of The World Avatar

Multi-criteria site selection using an ontology: the OntoZoning ontology of zones, land uses and programmes for Singapore

Data related to urban planning is diverse both in terms of sources and formats. To facilitate urban analyses and public access to regulatory information, greater data interoperability is needed. Semantic web technologies, which use ontologies to link diverse data, are a promising solution to this problem. In this paper, we describe OntoZoning, an ontology representing relationships between zoning types, land uses and programmes (more specific land uses) in Singapore. We link the ontology to geospatial data stored in a knowledge graph, which allows executing multi-domain queries on urban data. We demonstrate how such queries can improve access to urban data, and in particular facilitate site selection and exploration. These are common tasks in urban planning and urban development processes. We also discuss how certain parts of zoning regulations are difficult to represent through ontologies, and would likely need to be defined more explicitly to fully represent city planning knowledge digitally.ISSN:1473-427

A semantic web approach to land use regulations in urban planning: The OntoZoning ontology of zones, land uses and programmes for Singapore

Semantic web technologies have the potential to significantly improve urban regulatory data access, integration and usability, with potentially large implications for planning practice. Ontologies are a cornerstone of the semantic web. In this paper, we describe OntoZoning, an ontology representing relationships between zoning types, land uses and programmes (more specific land uses) in Singapore. We link the ontology to geospatial data stored in a knowledge graph, which allows executing multi-domain queries on urban data. We demonstrate how such a semantic web based approach can improve access to and usability of land use regulation data, and in particular facilitate site selection and exploration. We also discuss the difficulty of defining some concepts in the land use regulation field, and how OntoZoning could be linked to a broader semantic-web based urban planning regulatory framework.ISSN:2226-5856ISSN:2589-036

Semantic 3D City Database — An enabler for a dynamic geospatial knowledge graph

This paper presents a dynamic geospatial knowledge graph as part of The World Avatar project, with an underlying ontology based on CityGML 2.0 for three-dimensional geometrical city objects. We comprehensively evaluated, repaired and refined an existing CityGML ontology to produce an improved version that could pass the necessary tests and complete unit test development. A corresponding data transformation tool, originally designed to work alongside CityGML, was extended. This allowed for the transformation of original data into a form of semantic triples. We compared various scalable technologies for this semantic data storage and chose Blazegraph™ as it provided the required geospatial search functionality. We also evaluated scalable hardware data solutions and file systems using the publicly available CityGML 2.0 data of Charlottenburg in Berlin, Germany as a working example. The structural isomorphism of the CityGML schemas and the OntoCityGML Tbox allowed the data to be transformed without loss of information. Efficient geospatial search algorithms allowed us to retrieve building data from any point in a city using coordinates. The use of named graphs and namespaces for data partitioning ensured the system performance stayed well below its capacity limits. This was achieved by evaluating scalable and dedicated data storage hardware capable of hosting expansible file systems, which strengthened the architectural foundations of the target system

Creating multi-domain urban planning indicators using a knowledge graph: a district energy use case in Singapore

In Singapore, decision-makers from multiple ministries and government agencies participate in urban planning and management. This often results in siloed datasets, different data formats and domain specific software: a lack of interoperability that hinders the integration of (big) data in planning. Semantic Web Technology (SWT) can help to solve data interoperability issues. With SWT, computers can infer semantic relationships between heterogeneous data that are linked using ontologies (i.e. ‘common languages’). Knowledge Graph (KG) data structures allow such linking, and thus support SWT applications. The Cities Knowledge Graph (CKG) research project uses a KG to facilitate the use of multi-domain data in city planning. We present a use case that demonstrates how KGs enable the creation of planning indicators, building on various openly available datasets in Singapore. The first step was to transform datasets containing geospatial and regulatory data on zoning, parcels and buildings to CityGML. Then it was loaded into a KG structured using a CityGML-based ontology. Retrieving raw data values or composite metrics (essentially manifold combinations of queries on different datasets) was done using SPARQL. However, our goal was to develop indicators that could benefit urban planners – examples include ‘GPR potential’ (unused Gross Plot Ratio per zone) or ‘allowable programmes per plot’ (which uses could exist on a plot, given its zone). These and other indicators have many potential applications, including in urban energy modelling. For instance, we developed an indicator for district cooling potential based on geometric parcel data as well as zoning and density data. We demonstrated how these indicators can be used to analyse a part of downtown Singapore, and visualised the results. We show that KG technology allows planners to analyse cities through multi-domain indicators, which would be difficult to develop based on individual datasets. Another benefit of KGs is highly malleable data architecture, allowing data to be updated, expanded or created. Future work includes integrating new datasets into our CKG and developing new analyses. Ultimately, these could be carried out autonomously by a multi-agent system