VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain
Abstract
:1. Introduction
2. Methods
2.1. Decision-Making Framework: Addressing the Curse of Dimensionality
2.1.1. Visual Analytics
2.1.2. Cluster Analysis
2.2. Multi-Scale Urban Vulnerability Assessment
2.2.1. Urban Vulnerability Assessment Framework
- Unemployment rate. Percentage of unemployed individuals by all individuals currently in the labour force.
- Education index: Percentage of illiterate population and without education.
- Housing index: Percentage of population living in dwellings without bathroom or water closet within the dwelling.
2.2.2. Identification of the Multi-Scale Effect of Urban Vulnerability
- SVij is the state of vulnerability of the j entity at i scale with j ∈ (1, …, n), and i ∈ (1, …, m), where n is the number of entities at i scale, and m is the number of scales in the territory assessed.
- SVEij is the state of vulnerability’s evolution of the j entity at i scale
- CRLij represents the context’s risk level of the j entity at i scale, which would be low (CRL-Low), medium (CRL-Medium), or high (CRL-High) depending on whether the corresponding entity at the upper scale’s position was in the first, second, or last third of the risk rank at the upper scale.
3. Case Study: Assessing Urban Vulnerability in Spain
3.1. Information Collection Process
3.1.1. Quantitative Information
3.1.2. Qualitative Information
3.2. Running of the Process
- ■
- Value taken as a base of vulnerability
- ■
- Vulnerability threshold
- ■
- Vulnerability Approach
4. Results and Discussion
4.1. Evaluation of Expert’s Relative Preferences
4.2. Urban Vulnerability Assessment
4.2.1. Curse of Dimensionality
4.2.2. Results of the Assessment and Identification of Multi-Scale Context’s Effect
5. Discussion
5.1. Results of Expert Judgment
5.2. Results Yielded by the VisualUVAM Tool
6. Conclusions and Further Research
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Approach | Imp | Aspects | Imp | Indicators | ||||
---|---|---|---|---|---|---|---|---|
ID | Description | Importance | Rank | OUV’s Importance | ||||
Socio-Economic (SE) Aspects | 51.81 | Social Structure (SS) | 27.68 | 1 | Area Population | 3.64 | 11 | 0 |
2 | Population density (pop/ha) | 8.84 | 1 | 0 | ||||
3 | Eder 75 years or more (%) | 5.85 | 3 | 0 | ||||
4 | Households of one person older than 64 years (%) | 3.68 | 10 | 0 | ||||
5 | Households of one adult and at least one minor (%) | 5.67 | 4 | 0 | ||||
Economic Strucure (ES) | 11.49 | 6 | Unemployment rate (%) | 4.47 | 5 | 33 | ||
7 | Youth unemployment rate (%) | 1.99 | 21 | 0 | ||||
8 | Temporary employee (%) | 2.02 | 20 | 0 | ||||
9 | Unqualified workers (%) | 3 | 14 | 0 | ||||
Educational level (EL) | 12.64 | 10 | Population uneducated (%) | 4.24 | 6 | 33 | ||
11 | Population with primary education (%) | 3.8 | 8 | 0 | ||||
12 | Population with secondary education (%) | 1.75 | 24 | 0 | ||||
13 | Population with higher education (%) | 2.85 | 15 | 0 | ||||
Bio-Physical (BF) Aspects | 48.19 | Area Occupation (AO) | 9.6 | 14 | Number of dwellings(u) | 1.65 | 25 | 0 |
15 | Dwellings density (u/Ha) | 6.15 | 2 | 0 | ||||
16 | Area (Ha) | 1.8 | 22 | 0 | ||||
Dwellings Condition (DC) | 11.75 | 17 | Dwellings rate (% Population with no WC in the dwelling) | 1.05 | 31 | 33 | ||
18 | Dwellings in ruin condition (%) | 3.4 | 13 | 0 | ||||
19 | Dwellings in bad condition (%) | 2.6 | 17 | 0 | ||||
20 | Dwellings in deficient condition (%) | 1.77 | 23 | 0 | ||||
21 | Dwellings in good condition (%) | 1.29 | 30 | 0 | ||||
22 | Dwellings without running water (%) | 1.64 | 26 | 0 | ||||
Dwellings Size (DS) | 16.37 | 23 | Dwellings with less than 30 m2 (%) | 2.75 | 16 | 0 | ||
24 | Dwellings total usable surface (m2) | 2.5 | 18 | 0 | ||||
25 | Mean usable surface by dwelling (m2) | 3.88 | 7 | 0 | ||||
26 | Mean usable surface by habitant (m2) | 3.44 | 12 | 0 | ||||
27 | Number of rooms by dwelling (u/dwell) | 3.8 | 9 | 0 | ||||
Dwellings Usage (DU) | 5.63 | 28 | Main Dwellings (u) | 1.05 | 32 | 0 | ||
29 | Empty Dwellings (u) | 2.34 | 19 | 0 | ||||
30 | Owned Dwellings (u) | 0.76 | 34 | 0 | ||||
31 | Rented Dwellings (u) | 1.48 | 28 | 0 | ||||
Dwellings age (DA) | 4.84 | 32 | Dwellings in builds built before 1951 (%) | 1.49 | 27 | 0 | ||
33 | Total buildings (u) | 0.44 | 36 | 0 | ||||
34 | Buildings older than 30 years (u) | 0.51 | 35 | 0 | ||||
35 | Buildings older than 50 years (u) | 1.45 | 29 | 0 | ||||
36 | Buildings older than 80 years (u) | 0.96 | 33 | 0 |
ID | Province Name | Ranking | |||
---|---|---|---|---|---|
Risk | State | State Evolution | Context’s (*) Risk Level | ||
11 | Cádiz | 51 | 52 | 5 | 2 |
14 | Córdoba | 3 | 42 | 4 | 0 |
18 | Granada | 12 | 39 | 5 | 0 |
21 | Huelva | 21 | 44 | 8 | 1 |
23 | Jaén | 5 | 37 | 4 | 0 |
29 | Málaga | 39 | 48 | −1 | 2 |
4 | Almería | 52 | 43 | 14 | 2 |
41 | Sevilla | 8 | 50 | 0 | 0 |
22 | Huesca | 29 | 5 | 1 | 1 |
44 | Teruel | 33 | 2 | 0 | 1 |
50 | Zaragoza | 2 | 19 | −11 | 0 |
33 | Asturias | 35 | 27 | −8 | 2 |
38 | Santa Cruz de Tenerife | 46 | 47 | 4 | 2 |
7 | Balears | 16 | 34 | −10 | 0 |
35 | Palmas (Las) | 34 | 49 | 4 | 1 |
39 | Cantabria | 41 | 21 | −7 | 2 |
24 | León | 4 | 8 | −9 | 0 |
34 | Palencia | 25 | 4 | −3 | 1 |
37 | Salamanca | 48 | 15 | 1 | 2 |
40 | Segovia | 24 | 3 | 0 | 1 |
42 | Soria | 13 | 1 | 0 | 0 |
47 | Valladolid | 10 | 17 | −8 | 0 |
49 | Zamora | 32 | 6 | −3 | 1 |
5 | Ávila | 49 | 11 | 1 | 2 |
9 | Burgos | 40 | 7 | −6 | 2 |
13 | Ciudad Real | 9 | 24 | 1 | 0 |
16 | Cuenca | 50 | 16 | 10 | 2 |
19 | Guadalajara | 1 | 12 | 4 | 0 |
2 | Albacete | 15 | 23 | 3 | 0 |
45 | Toledo | 36 | 28 | 4 | 2 |
17 | Girona | 18 | 29 | −2 | 1 |
25 | Lleida | 26 | 9 | −2 | 1 |
43 | Tarragona | 42 | 32 | 0 | 2 |
8 | Barcelona | 38 | 38 | −13 | 2 |
12 | Castellón/Castelló | 37 | 26 | 0 | 2 |
3 | Alicante/Alacant | 43 | 41 | −5 | 2 |
46 | Valencia/València | 7 | 36 | −12 | 0 |
10 | Cáceres | 27 | 30 | 8 | 1 |
6 | Badajoz | 14 | 33 | −6 | 0 |
15 | Coruña (A) | 30 | 31 | −6 | 1 |
27 | Lugo | 23 | 14 | −2 | 1 |
32 | Ourense | 17 | 22 | 3 | 0 |
36 | Pontevedra | 28 | 35 | −5 | 1 |
28 | Madrid | 45 | 40 | −12 | 2 |
30 | Murcia | 22 | 45 | 3 | 1 |
31 | Navarra | 31 | 13 | −14 | 1 |
1 | Álava | 6 | 18 | −3 | 0 |
20 | Guipúzcoa | 47 | 20 | 2 | 2 |
48 | Vizcaya | 20 | 25 | −16 | 1 |
26 | Rioja (La) | 11 | 10 | 5 | 0 |
51 | Ceuta | 19 | 51 | 36 | 1 |
52 | Melilla | 44 | 46 | 34 | 2 |
Regression Model R-sq(adj): | 45.92% | |
---|---|---|
Predictors: | Coefficient | p-Value |
SV | −18.85 | 0.033 |
SVE | 102.07 | 0 |
CRL: | ||
Low–Medium | 13.1 | 0.552 |
High | 133.8 | 0 |
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Salas, J.; Yepes, V. VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain. Sustainability 2019, 11, 2191. https://doi.org/10.3390/su11082191
Salas J, Yepes V. VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain. Sustainability. 2019; 11(8):2191. https://doi.org/10.3390/su11082191
Chicago/Turabian StyleSalas, Jorge, and Víctor Yepes. 2019. "VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain" Sustainability 11, no. 8: 2191. https://doi.org/10.3390/su11082191
APA StyleSalas, J., & Yepes, V. (2019). VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain. Sustainability, 11(8), 2191. https://doi.org/10.3390/su11082191