scholarly journals Evaluating a Workflow Tool for Simplifying Scenario Planning with the Online WhatIf? Planning Support System

2020 ◽  
Vol 9 (12) ◽  
pp. 706
Author(s):  
Muhammad Qadeer ul Hussnain ◽  
Abdul Waheed ◽  
Khydija Wakil ◽  
Junaid Abdul Jabbar ◽  
Christopher James Pettit ◽  
...  
Keyword(s):  
User Experience ◽  
Smart Cities ◽  
Scenario Planning ◽  
Data Preparation ◽  
Planning Support Systems ◽  
Time Saving ◽  
Noticeable Increase ◽  
Planning Support ◽  
Planning Support System ◽  
Develop Land

In an era of smart cities and digitalisation, there has been a noticeable increase in the development and application of planning support systems (PSS). However, a significant challenge in the broader adoption of these PSS can be attributed to the user experience, which includes the efforts required in pre-processing data. It has been observed that typically 80% of the PSS usage time goes into pre-processing, cleaning, and loading data—a significant barrier for new users. This research focuses on improving user experience by developing and evaluating a new workflow tool called EasyUAZ. This workflow tool directly supports the iterative data preparation needs of scenario planning with the Online WhatIf?—a widely used PSS to develop land-use suitability, demand and land-allocation scenarios. A comparative evaluation has been conducted to quantify the time taken for data preparation with ArcGIS, QGIS, and the EasyUAZ. The study found that EasyUAZ offers a time saving of 30%–35% when compared with other options.

scholarly journals A new toolkit for land value analysis and scenario planning

2020 ◽  
Vol 47 (8) ◽  
pp. 1490-1507 ◽  
Author(s):  
Chris Pettit ◽  
Y Shi ◽  
H Han ◽  
M Rittenbruch ◽  
M Foth ◽  
...  
Keyword(s):  
Support System ◽  
Smart Cities ◽  
Scenario Planning ◽  
Hedonic Pricing ◽  
Least Square ◽  
Research Approach ◽  
Land Value ◽  
Value Analysis ◽  
Planning Support ◽  
Planning Support System

In the digital era of big data, data analytics and smart cities, a new generation of planning support systems is emerging. The Rapid Analytics Interactive Scenario Explorer is a novel planning support system developed to help planners and policy-makers determine the likely land value uplift associated with the provision of new city infrastructure. The Rapid Analytics Interactive Scenario Explorer toolkit was developed following a user-centred research approach including iterative design, prototyping and evaluation. Tool development was informed by user inputs obtained through a series of co-design workshops with two end-user groups: land valuers and urban planners. The paper outlines the underlying technical architecture of the toolkit, which has the ability to perform rapid calculations and visualise the results, for the end-users, through an online mapping interface. The toolkit incorporates an ensemble of hedonic pricing models to calculate and visualise value uplift and so enable the user to explore what if? scenarios. The toolkit has been validated through an iterative case study approach. Use cases were related to two policy areas: property and land valuation processes (for land taxation purposes) and value uplift scenarios (for value capture purposes). The cases tested were in Western Sydney, Australia. The paper reports on the results of the ordinary least square linear regressions – used to explore the impacts of hedonic attributes on property value at the global level – and geographically weighted regressions – developed to provide local estimates and explore the varying spatial relationships between attributes and house price across the study area. Building upon the hedonic modelling, the paper also reports the value uplift functionality of the Rapid Analytics Interactive Scenario Explorer toolkit that enables users to drag and drop new train stations and rapidly calculate expected property prices under a range of future transport scenarios. The Rapid Analytics Interactive Scenario Explorer toolkit is believed to be the first of its kind to provide this specific functionality. As it is problem and policy specific, it can be considered an example of the next generation of data-driven planning support system.

Lessons for developing a planning support system infrastructure: The case of Southern California's Scenario Planning Model

2017 ◽  
Vol 46 (4) ◽  
pp. 777-796 ◽  
Author(s):  
Robert Goodspeed ◽  
Cassie Hackel
Keyword(s):  
Support System ◽  
Participatory Design ◽  
Development Process ◽  
New Technologies ◽  
Support Systems ◽  
Scenario Planning ◽  
Planning Model ◽  
Planning Support Systems ◽  
Planning Support ◽  
Planning Support System

Although planning support systems are being more widely adopted by professional planners, there are very few examples of planning support system infrastructures designed to support planning practices on an ongoing basis. This paper reports the result of an exploratory qualitative study of the Southern California Association of Governments' Scenario Planning Model, an innovative new planning support system infrastructure. Interviews with professionals who served as participants in a two-year development process were conducted to explore the six dimensions that theories from the planning support systems, innovation diffusion, and organizational information technology fields suggest are important to understanding the adoption and use of a planning support system infrastructure: user considerations, perceived benefits, technical details, the development process, jurisdiction characteristics, and planning style. Drawing on these interviews, the article proposes seven lessons for the creation of planning support system infrastructures: utilize participatory design, support a variety of planning practices, address indirect costs to users, encourage collaboration among multiple users within each organization, ensure that all stakeholders have appropriate access, be mindful of the framing of new technologies, and embrace their transformational potential. Although the Scenario Planning Model has benefited from California's unique planning mandates, advances in web-based geospatial technologies mean that many regions may draw on these lessons to create similar planning support system infrastructures, which have the potential to improve local and regional planning practices through enhanced information, analysis, and communication.

Towards the collaborative development of machine learning techniques in planning support systems – a Sydney example

2020 ◽  
pp. 239980832093997 ◽  
Author(s):  
Oliver Lock ◽  
Michael Bain ◽  
Christopher Pettit
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support System ◽  
Support Systems ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Planning Support Systems ◽  
Planning Support ◽  
Learning Techniques ◽  
Planning Support System

The rise of the term ‘big data’ has contributed to recent advances in computational analysis techniques, such as machine learning and more broadly, artificial intelligence, which can extract patterns from large, multi-dimensional datasets. In the field of urban planning, it is pertinent to understand both how such techniques can advance our understanding of cities, and how they can be embedded within transparent and effective digital planning tools, known as planning support systems. This research specifically focuses on two related contributions. First, it investigates the role of planning support systems in supporting a participatory data analytics approach through an iterative process of developing and evaluating a planning support system environment. Second, it investigates how specifically machine learning planning support systems can be co-designed by built environment practitioners and stakeholders in this environment to solve a real planning issue in Sydney, Australia. This paper presents the results of applied research undertaken through the design and implementation of four workshops, involving 57 participants who were involved in a co-design process. The research follows a mixed-methods approach, studying a wide array of measures related to participatory analytics, task load, perceived added value, recordings and observations. The results highlight recommendations regarding the design and evaluation of planning support system environments for co-design and their coupling with machine learning techniques. It was found that consistency and transparency are highly valued and central to the design of a planning support system in this context. General attitudes towards machine learning and artificial intelligence as techniques for planners and developers were positive, as they were seen as both potentially transformative but also as simply another technique to assist with workflows. Some conceptual challenges were encountered driven by practitioners' simultaneous need for concrete scenarios for accurate predictions, paired with a desire for predictions to drive the development of these scenarios. Insights from this work can inform future planning support system evaluation and co-design studies, in particular those aiming to support democracy enhancement, greater inclusion and more efficient resource allocation through a participatory analytics approach.

Exploring the Potential for Planning Support Systems to Bridge the Research-translation Gap Between Public Health and Urban Planning

2021 ◽  
Author(s):  
Paula Hooper ◽  
Claire Boulange ◽  
Gustavo Arciniegas ◽  
Sarah Foster ◽  
Julian Bolleter ◽  
...  
Keyword(s):  
Built Environment ◽  
Urban Health ◽  
Support System ◽  
Health Impact Assessment ◽  
Health Impact ◽  
Planning Support Systems ◽  
Policy And Practice ◽  
Planning Support ◽  
Planning And Design ◽  
Planning Support System

Abstract Background: There is consensus that planning professionals need clearer guidance on the features that are likely to produce optimal community-wide health benefits. However, much of this evidence resides in academic literature and not in tools accessible to the diverse group of professionals shaping our cities. This paper explores the role of planning support systems (PSS) to facilitate the translation and application of health evidence into urban planning and design practices to create healthy, liveable communities.Methods: A review of PSS software and studies featuring a planning support system with a built environment and health impact assessment for designing and creating healthy urban areas was undertaken. Customising existing software, a health impact PSS (the Urban Health Check) was then developed and applied to a real-world planning application with an industry partner allowing for: (i) automated calculation of built environment variables; (ii) “sketch planning” functionality; and (iii) a health impact indicator that estimates the probability of walking for transport. A participatory evaluation framework evaluated the "utility", "usability", and "usefulness" of the Urban Health Check.Results: Eleven PSS software applications were identified, of which three were identified as having the capability to undertake health impact analyses. Three studies met the inclusion criteria of presenting a planning support system customised to support health impact assessment with health impacts modelled or estimated due to changes to the built environment. Evaluation results indicated the Urban Health Check PSS helped in four key areas: visualisation of how the neighbourhood would change in response to a proposed plan; understanding how a plan could benefit the community; Communicate and improve understanding health of planning and design decisions that positively impact health outcomes; engagement – allowing community members to provide direct feedback and see the immediate implications of amendments to a proposed plan. Conclusions: The use of health-impact PSS have the potential to be transformative for the translation and application of health evidence into planning policy and practice, providing those responsible for the policy and practice of designing and creating our communities with access to quantifiable, evidence-based information about how their decisions might impact community health.

From the Smart City to the People-Friendly City

2015 ◽  
pp. 363-381 ◽  
Author(s):  
Giulia Melis ◽  
Elena Masala ◽  
Matteo Tabasso
Keyword(s):  
Urban Environment ◽  
Smart City ◽  
Support Systems ◽  
Smart Cities ◽  
Planning Support Systems ◽  
Collaborative Process ◽  
Numerical Information ◽  
Planning Support ◽  
The People ◽  
Key Points

This chapter addresses the smart city concept as a first step towards the formulation of a new socially-improved urban concept which may be defined as that of the “people-friendly city”. This new task involves the employment of IT tools, but using new methods and pursuing different goals other than mere numerical information. In terms of the urban environment, this means that cities should be designed for people, and planning practitioners should be able to understand citizens' needs, communicate with them and involve them in a collaborative process. Therefore, an overview of the implications of smart cities for urban planning is followed by a more detailed analysis of Planning Support Systems (PSS) as innovative tools for enhancing the process of delivering a more inclusive and people-friendly urban environment. The lessons learnt from the application of the PSS tool is then illustrated in order to define the potentialities and key points for the development of similar tools.

Planning support systems for smart cities

2018 ◽  
Vol 12 ◽  
pp. 13-24 ◽  
Author(s):  
Christopher Pettit ◽  
Ashley Bakelmun ◽  
Scott N. Lieske ◽  
Stephen Glackin ◽  
Karlson ‘Charlie’ Hargroves ◽  
...  
Keyword(s):  
Support Systems ◽  
Smart Cities ◽  
Planning Support Systems ◽  
Planning Support

From the Smart City to the People-Friendly City

2019 ◽  
pp. 758-777
Author(s):  
Giulia Melis ◽  
Elena Masala ◽  
Matteo Tabasso
Keyword(s):  
Urban Environment ◽  
Smart City ◽  
Support Systems ◽  
Smart Cities ◽  
Planning Support Systems ◽  
Collaborative Process ◽  
Numerical Information ◽  
Planning Support ◽  
The People ◽  
Key Points

This chapter addresses the smart city concept as a first step towards the formulation of a new socially-improved urban concept which may be defined as that of the “people-friendly city”. This new task involves the employment of IT tools, but using new methods and pursuing different goals other than mere numerical information. In terms of the urban environment, this means that cities should be designed for people, and planning practitioners should be able to understand citizens' needs, communicate with them and involve them in a collaborative process. Therefore, an overview of the implications of smart cities for urban planning is followed by a more detailed analysis of Planning Support Systems (PSS) as innovative tools for enhancing the process of delivering a more inclusive and people-friendly urban environment. The lessons learnt from the application of the PSS tool is then illustrated in order to define the potentialities and key points for the development of similar tools.

From the Smart City to the People-Friendly City

2018 ◽  
pp. 679-697
Author(s):  
Giulia Melis ◽  
Elena Masala ◽  
Matteo Tabasso
Keyword(s):  
Urban Environment ◽  
Smart City ◽  
Support Systems ◽  
Smart Cities ◽  
Planning Support Systems ◽  
Collaborative Process ◽  
Numerical Information ◽  
Planning Support ◽  
The People ◽  
Key Points

This chapter addresses the smart city concept as a first step towards the formulation of a new socially-improved urban concept which may be defined as that of the “people-friendly city”. This new task involves the employment of IT tools, but using new methods and pursuing different goals other than mere numerical information. In terms of the urban environment, this means that cities should be designed for people, and planning practitioners should be able to understand citizens' needs, communicate with them and involve them in a collaborative process. Therefore, an overview of the implications of smart cities for urban planning is followed by a more detailed analysis of Planning Support Systems (PSS) as innovative tools for enhancing the process of delivering a more inclusive and people-friendly urban environment. The lessons learnt from the application of the PSS tool is then illustrated in order to define the potentialities and key points for the development of similar tools.

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