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Eindhoven University of Technology MASTER Urban planning indicators in spatial decison support systems Valk, R. Award date: 2016 Link to publication Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain URBAN PLANNING INDICATORS IN SPATIAL DECISION SUPPORT SYSTEMS ROB VALK URBAN PLANNING INDICATORS IN SPATIAL DECISION SUPPORT SYSTEMS MASTER GRADUATION FINAL REPORT R. (Rob) Valk BSc1 Eindhoven University of Technology, Faculty of the Built Environment Master tracks ‘Urban Design and Planning’ and ‘Design Systems’ Student number: 0725739 Course 7MM37 (Final project DDSS) Supervisors: prof.dr.ir. B. (Bauke) de Vries2, ir. A.W.J. (Aloys) Borgers3, T. (Tong) Wang4, ir. H. (Hedwig) van Delden5 In collaboration with Research Institute for Knowledge Systems (RIKS). June 2016 1 [email protected] 2 Eindhoven University of Technology, Faculty of the Built Environment. [email protected] 3 Eindhoven University of Technology, Faculty of the Built Environment. [email protected] 4 Eindhoven University of Technology, Faculty of the Built Environment. [email protected] 5 Research Institute for Knowledge Systems (RIKS) and University of Adelaide. [email protected] SUMMARY This research focusses on contributing to the applicability of spatial decision support systems (SDSSs) for urban designers and planners, by leveraging indicators. It reveals that an important gap in literature is the general lack of indicator sets or indicator catalogues that are suitable to support urban design and planning (UDP) professionals in their complex, integral, and multi-thematic design, planning, and policy making. Additionally, few of such indicators are consistently implemented in existing SDSS applications or planning support system (PSS) applications. Therefore, this study contributes to the existing research with a theoretical inventory of currently used indicators and indicator topics, as well as, a more practical documentation of the implementation of such indicators in an existing SDSS. Herein, an indicator can be defined as a value, statistic or parameter which conveys information about a phenomenon and thereby describes (properties of) a system or subsystem more substantial than the value itself (EEA, 2002a). An initial review of literature identified that few comprehensive and non-thematic sets of (local level) indicators for urban designers and planners are available. Therefore, in this research a novel and extensive literature study is conducted that identifies common indicators, indicator topics, and their structures, which are suitable for UDP professionals within different contexts. Afterwards, the UDP-relevant concept of ‘Quality of Life’ (QOL) is introduced to frame the further work, and interviews are conducted to practically embed the theoretical work. Finally, a number of selected indicators are documented, implemented, and showcased for use in an existing SDSS. The goal of this latter step is to detail how SDSSs can be made more suitable to urban designers and planners by utilizing indicators. Firstly, from the novel and extensive literature review, it can be concluded that current literature is generally thematic and hardly any wide viewpoint is taken. Many authors organize their indicators into thematic clusters (‘dimensions’); however, they seldom discuss the full width of the possible UDP spectrum. In this review, the most commonly found dimensions are environmental, social, and economic dimensions and somewhat less frequent a physical dimension. As well, a legal and demographic dimension are separately considered in this study to reflect a number of more detailed structures of dimensions. The found topics among these dimensions are most common among the physical and demographic dimensions, while the environmental dimension contains many different but infrequent topics. The other dimensions contain less common topics. Behind this main structure is a large number of indicators. Of all these dimensions, topics, and indicators the most common ones are summarized (overview of dimensions and topics in Table 1). By contrast, most found sources do not cover this whole broad range of dimensions, topics, and indicators. Additionally, considering the ‘context’ of an indicator set/catalogue, both the thematic background, the goal, and the scale seem to have little general relation to the used dimensions. However, detailed ‘non-aggregated’1 sets and other sets that focus on spatially-explicit output lack a number of topics and indicators that other sets do cover. ‘Indicator projects’, on the other hand, frequently cover a wider range of dimensions than ordinary sources, yet most remain to have a thematic viewpoint. Thus, in this study common topics and indicators and common methods of categorization are identified for the wide scope of UDP professionals. However, complete and common sets of indicators are absent in the reviewed sources. Additionally, limited documentation often impedes easy reuse of found indicators. Furthermore, it is frequently described that expert knowledge is considered indispensable in indicator studies. 1 In this context, non-aggregated sets/studies do not aggregate the indicators for large areas such as neighborhoods. Summary Rob Valk – 2016-06 v Secondly, the concept of ‘Quality of Life’ (QOL) – given its relevance to UDP – is used to prioritize and direct the further work on SDSS improvement from the broad literature study. Nine important QOL dimensions are identified: environment, transportation/mobility, services and facilities, education, health, recreation, economic conditions, safety/risk, and social conditions/community. These are used to organize the relevant and spatially-explicit UDP indicators found in literature. Additionally, interviews with UDP professionals confirm the finding from literature that multiple indicator categorizations are possible and a broad spectrum of topics is relevant to UDP. Furthermore, these professionals describe development/evaluation of scenarios and explaining their choices as important desired functions of SDSSs. Thirdly, the ‘practical’ documentation of the implementation of a number of indicators results in suggestions and descriptions for improving SDSSs with indicators applicable to urban designers and planners. This details the relevant context, required data, the algorithm, resulting outcome, and an extensive discussion; lastly a hypothetical showcase is given. Indicators are selected that are feasible to implement in the utilized SDSS Metronamica (example of its land use map in Figure 1), which is a cellular automata land use change model.1 A ‘shortest distance to facilities’ indicator describes the distance between residential land uses and selected land uses; a useful extension is the ‘population within range’ indicator that describes the percentage of the population that resides within a set range of the selected land uses. An existing indicator highlighting land use changes has been extended to identify use of protected land. One alternative uses (mostly available) zoning maps, another requires additional maps of specific protected areas. For a set of ‘traffic noise nuisance’ and ‘air pollution’ indicators, numerous assumptions and simplifications need to be made. Although an underpinned alternative for road traffic noise nuisance is implemented, these indicators are rather complicated and troublesome and reveal few changes in the showcase. Lastly, alternatives are described for indicators of the ‘amount of a certain land use per capita (in the proximity)’. A straightforward alternative is implemented that describes the zonal area of a certain land use type per capita. A second, more spatially-explicit, alternative details this ratio for people and land use within a specified distance (example in Figure 2). These indicators can identify a number of aspects that are relevant to both QOL and UDP, as the showcase also reveals. Therefore, their implementations, as well as, their constructed documentations and suggested alternatives, limitations, and possibilities can make SDSSs more suitable to urban designers and planners. Finally, from the aforementioned findings it is concluded that the current literature on UDP indicators is varied and lacks consensus about which topics and indicators are relevant. Additionally, the implementation of indicators has revealed a great diversity in complexity, comprehensibility, and feasibility of different indicators. Nonetheless, a number of useful applications of QOL- and UDP-relevant indicator applications are possible. A common set of indicators has not only not been found, its existence has also been disputed due

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