Abstract

We present a Geographical Information System (GIS)-based framework implementing a Mamdani fuzzy rule-based system to partition in an unsupervised mode an urban system in urban green areas. The proposed framework is characterized by high usability and flexibility. The study area is partitioned into homogeneous regions regarding the characteristics of public green areas and relations with the residents and buildings. The urban system is initially partitioned into microzones, given the smallest areas in which a census of the urban system is taken in terms of resident population, type and number of buildings and properties, and industrial and service activities. During a pre-processing phase, the values of specific indicators defined by a domain expert, which characterize the type of urban green area and the relationship with the residents and buildings, are calculated for each microzone. Subsequently, the fuzzy rule-based system component is executed to classify each microzone based on the fuzzy rule set constructed by the domain expert. Spatially adjoining microzones belonging to the same class are dissolved to form homogeneous areas called urban green contexts. The membership degrees of the microzones to the fuzzy set of their class are used to evaluate the reliability of the classification of the urban green context. We test our framework on the municipality of Pozzuoli, Italy, comparing the results with the ones obtained in a supervised manner by the expert appropriately partitioning and classifying the urban study area based on his knowledge of it.

Highlights

  • In order to make urban areas more resilient to the effects of climate change, it is necessary to safeguard the presence of urban green areas and plan adaptation actions for future climate scenarios, such as, for example, applying green strategies to facilitate energy efficiency in metropolitan areas [1].The need to transform urban systems into areas more resilient to phenomena generated by climate change makes a detailed study of the characteristics of urban green areas, relationships with buildings and relationships with the social fabric a crucial activity for the planner

  • After classifying all microzones we use the spatial dissolve operator to aggregate adjoint microzones belonging to the same UGA class, to detect the urban green contexts (UGCs) corresponding to regions of the study area that are homogeneous with respect to the characteristics of the urban greenery and its relations with the buildings and social fabric

  • To evaluate the reliability of the UGC map, based on the concept of fuzzy reliability of the partitioning of a geographic area introduced in [18] and following the process applied for evaluating the reliability of the urban context map proposed in [7], we generate a reliability map in which the reliability of a UGC is calculated as a weighted average of the membership degree of the output class of the microzones spatially included in the UGC

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Summary

Introduction

In order to make urban areas more resilient to the effects of climate change, it is necessary to safeguard the presence of urban green areas and plan adaptation actions for future climate scenarios, such as, for example, applying green strategies to facilitate energy efficiency in metropolitan areas [1]. After classifying all microzones we use the spatial dissolve operator to aggregate adjoint microzones belonging to the same UGA class, to detect the urban green contexts (UGCs) corresponding to regions of the study area that are homogeneous with respect to the characteristics of the urban greenery and its relations with the buildings and social fabric. To evaluate the reliability of the UGC map, based on the concept of fuzzy reliability of the partitioning of a geographic area introduced in [18] and following the process applied for evaluating the reliability of the urban context map proposed in [7], we generate a reliability map in which the reliability of a UGC is calculated as a weighted average of the membership degree of the output class of the microzones spatially included in the UGC.

The GIS-Based UGC Classification Framework
Microzone Dataset Creation
Fuzzy Rule Set Creation
Mamdani Fuzzy Rule System Execution
Urban Context Partitioning
The GIS-Based UGC Classification Processes
Microzone Dataset Creation—Calculus of Indicators by Microzone
UGC Partitioning
Test Results
Final Considerations
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