A criterion for modelling the ‘live-and-work’ city index using sustainable development indicators

Fouda, Yasser ElSayed; Elkhazendar, Deena Mohamed

doi:10.1080/19463138.2018.1556161

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…The research went through various numerous projects that exist worldwide to develop and apply indicators, some of them are; The Canadian Mortgage and Housing Corporation (CMHC), the United Nations Development Programme (UNDP), Eurostat, the UN Commission on Sustainable Development and the Habitat Centre of the United Nations. Further details were also established in previous published papers [40,41].…”

Section: Figure 3 Action Methodology Chart For Model Frameworkmentioning

confidence: 99%

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Spatio-Temporal Analysis to Dynamically Map and Audit Urban Growth in Peri-Urban Areas

Fouda¹,

Elkhazendar²

2021

IJSDP

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Villages located today in the peri-urban area, are in continuous agglomeration. The lack of proper guidelines and monitoring systems to define, promote, regulate and manage the pattern of development in these areas, causes massive deteriorations to the environment and quality of life. In Egypt, very few states have a dedicated legal framework that addresses the need for planned development in the peri-urban areas. A Spatio-temporal model framework consisting of a set of geospatial indicators is required to regulate and direct the growth and development of these areas and prevent further spill across administrative boundaries. This article responds to the lack of a geo-spatial quantifiable criterion as a key to detect, analyze, and better govern spatial and temporal patterns of urban growth in peri-urban areas, where the results are to be represented dynamically in forms of spatial patterns evolving in time. The research aims to develop a process-wise, contextual mapping of peri-urban dynamics using GIS to detect and analyze spatial and temporal patterns of urban growth. It has been applied to one of the peri-urban areas in Egypt, in Meet Assas Village, chosen as a case study. Miscellaneous primary and secondary data sources together with the methods used for monitoring: UN Criteria (Goal 11) targets, thematic headlines, village’s problems and geo-spatial indicators were the main constitutions of the model frame work. The results show that the village is characterized by a dispersed pattern of development with the absence of basic infrastructure and services. Besides that, the study reveals, through temporal patterns, that the future and unplanned growth will continue to intensify; posing numerous threats on the environment and the quality of life. A harmonized standardized measurement framework for planning, development, and management is crucial rather than demolishing these areas.

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Section: Figure 3 Action Methodology Chart For Model Frameworkmentioning

confidence: 99%

“…• Have spatial dimensions • Integrate the interlinkages between economic, social and environmental dimensions of sustainable development • Are valid and reliable • Data availability [40,41].…”

Section: Figure 3 Action Methodology Chart For Model Frameworkmentioning

confidence: 99%

Spatio-Temporal Analysis to Dynamically Map and Audit Urban Growth in Peri-Urban Areas

Fouda¹,

Elkhazendar²

2021

IJSDP

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“…The simplest way to reduce SDG indicators is found in the 'live and work city' index which only includes 10 main goals out of the 17 goals of the SDGs. The reason is to reduce the size of the separate indicators and visualize all the targeted values of the individual indicators and the sub-indices to make it easier to track the progress toward the targeted values (Fouda & Elkhazendar 2019). The African research on agriculture SDG index relies on data for indicators from five SDGs (SDGs 1, 2, 6, 7, and 15).…”

Section: Presenting Indicators Of Achievement As a Composite Indexmentioning

confidence: 99%

Indonesian Provinces SDGs Composite Index: Lampung Province Analysis

Andriati

Yusuf

2021

ECON FINANC INDONES

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Publications of Sustainable Development Goals (SDGs) have mainly been conducted at a national level and separately for each goal. No prior research has been done on SDGs composite index at a provincial level in Indonesia. It is necessary to create a composite index that presents a single value at the provincial level to enable regional evaluation. The Indonesia Province SDGs composite index is developed from indicators based on Statistics Indonesia gathered from several publications. The data sources are the National Socio-Economic Survey (Susenas) and the Basic Health Research (Riskesdas) which were linked surveys held in 2018. Principal Component Analysis and Factor Analysis are used as the methods to select the indicators of the SDGs. Those selected indicators are then normalized using the min-max method and subsequently weighted using factor loading derived from the principal component analysis. Finally, the indicators are aggregated using an arithmetic mean to determine the composite index. The Indonesia Province SDGs composite index is an approach to measure achievement of SDGs agenda. In addition, each goal achievement is summarized as a goal index. The SDGs composite index for Lampung Province is 52.2%, meaning that Lampung Province is 52.2% of the way to fully achieving the SDGs, according to the measures used to calculate this index. The findings on goal index suggest that development is highly requested on public services such as housing and water supply.

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“…[20]. Meanwhile, it can also account for specific characteristics of the spatial structure and internal drivers of urban development, including urban resource endowments, infrastructure, public services, and population distribution [21]. Compared to the spatial simulation method, the comprehensive evaluation method has the advantages of intuitiveness, clearness, and good operability.…”

Section: Introductionmentioning

confidence: 99%

Integrating Multisource Geographic Big Data to Delineate Urban Growth Boundary: A Case Study of Changsha

Gao,

Xia,

Zhuang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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An urban growth boundary (UGB) is an important policy tool used to control urban sprawl, which can effectively balance the urban construction needs, residents' quality of life, and urban ecological protection. Current studies of UGB delineation and its indicators have paid little attention to human factors such as human activities and economic vitality, and weights for evaluation indicators have been determined highly subjectively. In response to these problems, this study integrated multi-source geographic big data to construct a total of 30 natural, human, and ecological evaluation indicators. The GIS technology and machine learning (ML) approach were combined to determine indicator weights with an officially manually drawn 2035 UGB as the reference, aiming to reduce the subjectivity. The suitability score was then calculated from indicator grading 4-1 and related weights, and high suitability (>2.15) regions were eventually delineated as the UGB. Results showed a delineated UGB of 1,528.06 km 2 with an overall accuracy of over 93% and high consistence with reference data for 2035 in the Chinese city of Changsha. The geographic big data totally contributed more than 33.72%, which mainly characterized role of human elements, and a 5-6 percentage point reduction in accuracy was found without these data. Compared with existing studies, our delineated UGB had higher accuracy and closer spatial pattern to the reference data, verifying the effectiveness and reasonability of ML-based weight setting approach and index system with geographic big data. The proposed method can provide scientific and accurate framework for UGB delineation, which can promote the territorial spatial planning and sustainable urban development.

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A criterion for modelling the ‘live-and-work’ city index using sustainable development indicators

Cited by 9 publications

References 5 publications

Spatio-Temporal Analysis to Dynamically Map and Audit Urban Growth in Peri-Urban Areas

Spatio-Temporal Analysis to Dynamically Map and Audit Urban Growth in Peri-Urban Areas

Indonesian Provinces SDGs Composite Index: Lampung Province Analysis

Integrating Multisource Geographic Big Data to Delineate Urban Growth Boundary: A Case Study of Changsha

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