Operationalizing the circular city model for naples' city-port: A hybrid development strategy

The city-port context involves a decisive reality for the economic development of territories and nations, capable of significantly influencing the conditions of well-being and quality of life, and of making the Circular City Model (CCM) operational, preserving and enhancing seas and marine resources in a sustainable way. This can be achieved through the construction of appropriate production and consumption models, with attention to relations with the urban and territorial system. This paper presents an adaptive decision-making process for Naples (Italy) commercial port's development strategies, aimed at re-establishing a sustainable city-port relationship and making Circular Economy (CE) principles operative. The approach has aimed at implementing a CCM by operationalizing European recommendations provided within both the Sustainable Development Goals (SDGs) framework-specifically focusing on goals 9, 11 and 12-and the Maritime Spatial Planning European Directive 2014/89, to face conflicts about the overlapping areas of the city-port through multidimensional evaluations' principles and tools. In this perspective, a four-step methodological framework has been structured applying a place-based approach with mixed evaluation methods, eliciting soft and hard knowledge domains, which have been expressed and assessed by a core set of Sustainability Indicators (SI), linked to SDGs. The contribution outcomes have been centred on the assessment of three design alternatives for the East Naples port and the development of a hybrid regeneration scenario consistent with CE and sustainability principles. The structured decision-making process has allowed us to test how an adaptive approach can expand the knowledge base underpinning policy design and decisions to achieve better outcomes and cultivate a broad civic and technical engagement, that can enhance the legitimacy and transparency of policies

MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities

Urbanism is no longer planned on paper thanks to powerful models and 3D simulation platforms. However, current work is not open to the public and lacks an optimisation agent that could help in decision making. This paper describes the creation of an open-source simulation based on an existing Dutch liveability score with a built-in AI module. Features are selected using feature engineering and Random Forests. Then, a modified scoring function is built based on the former liveability classes. The score is predicted using Random Forest for regression and achieved a recall of 0.83 with 10-fold cross-validation. Afterwards, Exploratory Factor Analysis is applied to select the actions present in the model. The resulting indicators are divided into 5 groups, and 12 actions are generated. The performance of four optimisation algorithms is compared, namely NSGA-II, PAES, SPEA2 and eps-MOEA, on three established criteria of quality: cardinality, the spread of the solutions, spacing, and the resulting score and number of turns. Although all four algorithms show different strengths, eps-MOEA is selected to be the most suitable for this problem. Ultimately, the simulation incorporates the model and the selected AI module in a GUI written in the Kivy framework for Python. Tests performed on users show positive responses and encourage further initiatives towards joining technology and public applications.Comment: 16 page

Simulating city growth by using the cellular automata algorithm

The objective of this thesis is to develop and implement a Cellular Automata (CA) algorithm to simulate urban growth process. It attempts to satisfy the need to predict the future shape of a city, the way land uses sprawl in the surroundings of that city and its population. Salonica city in Greece is selected as a case study to simulate its urban growth. Cellular automaton (CA) based models are increasingly used to investigate cities and urban systems. Sprawling cities may be considered as complex adaptive systems, and this warrants use of methodology that can accommodate the space-time dynamics of many interacting entities. Automata tools are well-suited for representation of such systems. By means of illustrating this point, the development of a model for simulating the sprawl of land uses such as commercial and residential and calculating the population who will reside in the city is discussed

Key challenges in agent-based modelling for geo-spatial simulation

Agent-based modelling (ABM) is fast becoming the dominant paradigm in social simulation due primarily to a worldview that suggests that complex systems emerge from the bottom-up, are highly decentralised, and are composed of a multitude of heterogeneous objects called agents. These agents act with some purpose and their interaction, usually through time and space, generates emergent order, often at higher levels than those at which such agents operate. ABM however raises as many challenges as it seeks to resolve. It is the purpose of this paper to catalogue these challenges and to illustrate them using three somewhat different agent-based models applied to city systems. The seven challenges we pose involve: the purpose for which the model is built, the extent to which the model is rooted in independent theory, the extent to which the model can be replicated, the ways the model might be verified, calibrated and validated, the way model dynamics are represented in terms of agent interactions, the extent to which the model is operational, and the way the model can be communicated and shared with others. Once catalogued, we then illustrate these challenges with a pedestrian model for emergency evacuation in central London, a hypothetical model of residential segregation tuned to London data which elaborates the standard Schelling (1971) model, and an agent-based residential location built according to spatial interactions principles, calibrated to trip data for Greater London. The ambiguities posed by this new style of modelling are drawn out as conclusions

A collaborative-project memory tool for participatory planning

Technology is more and more providing planners and designer with tools and methods to collect and communicate spatial data and assist spatial analysis. When we think about new technologies supporting planning we mainly think about GIS, urban modelling, simulation models and virtual reality. But many other challenges to the planning practice need for tools to support and improve planning activities. In this paper we discuss the need of new tools to support knowledge representation and knowledge sharing in participatory planning processes. The paper describes the use of a hypermedia and sensemaking tool (Compendium) to structure the knowledge produced in a real participatory planning process. In the present application Compendium has been used not for real-time capturing but for a post-hoc analysis of a real participatory planning experience. Compendium has been used to represent and reconstruct the group memory of consultation meetings in order to allow both the planning team and the citizens to navigate into the contents of those meetings. Moreover the paper describes the main features and potential of the use of Compendium in Participatory Planning domain, and it describes the results of the group memory reconstruction. Finally the case study opens reflections on the need of new planning technologies supporting participatory knowledge generation, representation and management

The Repast Simulation/Modelling System for Geospatial Simulation

The use of simulation/modelling systems can simplify the implementation of agent-based models. Repast is one of the few simulation/modelling software systems that supports the integration of geospatial data especially that of vector-based geometries. This paper provides details about Repast specifically an overview, including its different development languages available to develop agent-based models. Before describing Repast’s core functionality and how models can be developed within it, specific emphasis will be placed on its ability to represent dynamics and incorporate geographical information. Once these elements of the system have been covered, a diverse list of Agent-Based Modelling (ABM) applications using Repast will be presented with particular emphasis on spatial applications utilizing Repast, in particular, those that utilize geospatial data

Principles and Concepts of Agent-Based Modelling for Developing Geospatial Simulations

The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded. The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded

Derivation of robust predictor variables for modelling urban shrinkage and its effects at different scales

Currently, we observe diverging processes of growth and shrinkage in European Cities. Whereas in the 80ies and 90ies partially accelerated through the crash of the socialist system mostly urban growth and suburban development occurred in European Cities, today we find a general decline of population as well as an increase of aged people (as results of the demographic change in Europe and worldwide, Cloet 2003, Lutz 2001). These processes influence land use pattern (state of the environment) and land use changes in urban areas enormously. Land use pattern reflect the current socio-economic development of an urban area and give an idea of how the urban ecosystem is influenced by man. In doing so, for instance, surface sealing reduces the filtering and remediation capacity of soils and the water retention in general as well as minimises habitat quality for wetland species. At the same time, the ecosystem(s) provide so-called ecosystem services, benefits people obtain from ecosystems: water availability, drinking water, remediation and filtering of waste, places to settle, recreation facilities in nature and others. Their quantification enables to bring the change (availability/loss) of ecosystem services into relation with effective costs (economic sphere, Farber 2002, De Groot et al. 2002). The above mentioned population decline and related shrinkage processes will have enormous consequences on the demand and availability of ecosystem services needed to sustain a high and even increasing status of quality of life for European citizens in the next future. Therefore, the predictor variables describing on the one hand shrinkage-related land use changes and on the other its effects are most important but at the same time it is still a challenge; to extract such predictor variables from a huge catalogue of urban socio-economic and environmental indicators elaborated by many studies for different landscape types and scales; to derive relevant digital and spatially explicit data as model input to calculate the effects of land use (change) and; to validate the model results at the city and the quarter level (scale) as well as to prove the response of the (gained/released) ecosystem service (environmental quality) at the city and at quarter level (closing the circle). Here, the author will give some expressive examples showing the derivation of predictor variables for modelling peri-urban growth and inner city shrinkage as well as its effects on water balance, habitat quality (urban green network) and recreational space. Of major interest is the approach of how to tackle the problem of urban shrinkage in spatially explicit land use (change) modelling (Haase et al. 2004).

Cities and energy:urban morphology and residential heat-energy demand

Our aim is better understanding of the theoretical heat-energy demand of different types of urban form at a scale of 500 m × 500 m. The empirical basis of this study includes samples of dominant residential building typologies identified for Paris, London, Berlin, and Istanbul. In addition, archetypal idealised samples were created for each type through an analysis of their built form parameters and the removal of unwanted ‘invasive’ morphologies. The digital elevation models of these real and idealised samples were run through a simulation that modelled solar gains and building surface energy losses to estimate heat-energy demand. In addition to investigating the effect of macroscale morphological parameters, microscale design parameters, such as U-values and glazing ratios, as well as climatic effects were analysed. The theoretical results of this study suggest that urban-morphology-induced heat-energy efficiency is significant and can lead to a difference in heat-energy demand of up to a factor of six. Compact and tall building types were found to have the greatest heat-energy efficiency at the neighbourhood scale while detached housing was found to have the lowest

Simulation of suburban migration: driving forces, socio-economic characteristics, migration behaviour and resulting land-use patterns

Land-use transitions in metropolitan areas have a high impact on environment and appear as pressures on the inhabitants' living conitions. Tools are needed to support planning decisions to overcome or at least mitigate those pressures. Simulation models are such tools, generating land-use change scenarios that help to examine effects of planning strategies. This article introduces a model that establishes a multiagent system approach to achieve results for changes in land-use and migration patterns with high spatial accuracy. Details of suburban migration behaviour modelling are described with emphasis on the definition of socio-economic classes, on the detection of driving forces triggering suburban migration and on migration behaviour aspects with respect to those socio-economic classes. The model concept is presented as well as results of retrospective simulation runs for a 30-year time range that are compared with the observations of the simulation target year in order to examine the model's validity. Future scenario runs show different urban sprawl trends with either restricted or unlimited residential area zoning and higher versus lower target residential density regulations. A remarkable decrease of suburban sprawl can be achieved by applying the right planning measures, even if the numbers of migrating households remain the same.