Linkages between Typologies of Existing Urban Development Patterns and Human Vulnerability to Heat Stress in Lahore
Abstract
:1. Introduction
- What are the typologies of existing development patterns for Lahore based on urban configuration (settlement types, density) and urban composition (land-use pattern)?
- What is the spatial variability of human vulnerability to heat in Lahore?
- What are the linkages between urban development patterns/structures and human vulnerability to heat stress in different urban settings?
2. Theoretical and Conceptual Perspectives on Typologies of Urban Development Pattern and Human Vulnerability
2.1. Typologies of Urban Development Pattern, Human Vulnerability, and Their Interdependence
2.2. Conceptual Framework
3. Contextual Analysis of Lahore
4. Data and Methods
4.1. Typologies of Urban Development Pattern in the Case Study
4.2. Human Heat Vulnerability Assessment
4.2.1. Data Collection and Normalization
4.2.2. Data Aggregation and Index Development
4.3. Interrelation between Typologies of Urban Development Patterns and Human Heat Vulnerability
5. Results
5.1. Clustering of the Typologies of Urban Development Pattern
5.2. Hotpots of Human Heat Vulnerability
- Vulnerability hotspot A: Areas around the Ravi river course, formerly an industrial district and nowadays predominantly inhabited by a working-class population. In these locations, the susceptibility index is relatively high and coping and adaptive capacities are quite low.
- Vulnerability hotspot B: Undeveloped old parts of the city that lack green places. Moreover, the population and building densities are significantly higher in these areas.
- Vulnerability hotspot C: Recently and rapidly urbanizing areas of the city performing poorly in terms of coping capacities. The access to natural and social infrastructures is also quite low in these locations.
5.3. Links between Human Heat Vulnerability and Typologies of Urban Development Pattern
6. Discussion, Conclusions, and Outlook
6.1. Relevance of Development Patterns for Human Heat Vulnerability
6.2. Study Limitations
6.3. Transferability of the Methodology and Future Research Potentials
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Characteristics | Undeveloped Areas/Informal Areas | Planned Areas/Formal Areas |
---|---|---|
Density | High | Low–medium |
Pattern of houses | Irregular | Uniform |
Building line ** | Less than 5 ft. | 5 ft. to 20 ft. or more * |
Layout of roads | Irregular layout pattern | Uniform layout pattern |
Width of streets | Less than 30 ft. | 30 ft. to 220 ft. or more * |
Number of green spaces | Fewer | More |
Plot size | Max. 5 marla 1 marla = 225 sq. feet (Lahore City) 1 marla = 272.5 sq. feet (other cities) | 5 Marla to 4 Kanal or more * |
Appendix B
Appendix C
NO. | UC | Exposure Index | Susceptibility Index | Capacity Index | Vulnerability Index | Vulnerability Rank |
---|---|---|---|---|---|---|
1 | Cantonment | 0.59 | 0.45 | 0.45 | 0.00 | Lower |
2 | Bhangali | 0.31 | 0.44 | 0.44 | 0.00 | Lower |
3 | Anarkali | 0.68 | 0.35 | 0.35 | 0.01 | Lower |
4 | Sanda Kalan | 0.82 | 0.33 | 0.32 | 0.01 | Lower |
5 | Chohan Park | 0.44 | 0.35 | 0.34 | 0.01 | Lower |
6 | Darogha Wala | 0.46 | 0.52 | 0.50 | 0.01 | Lower |
7 | Qila Gujjar Singh | 0.74 | 0.36 | 0.34 | 0.02 | Lower |
8 | Bilal Park | 0.82 | 0.45 | 0.44 | 0.02 | Lower |
9 | Jinnah Hall | 0.72 | 0.35 | 0.34 | 0.02 | Lower |
10 | Gujjar Pura | 0.41 | 0.34 | 0.32 | 0.02 | Lower |
11 | Railway Colony | 0.74 | 0.43 | 0.45 | 0.03 | Lower |
12 | Muslim Abad | 0.42 | 0.54 | 0.51 | 0.04 | Lower |
13 | Barki | 0.38 | 0.51 | 0.55 | 0.04 | Lower |
14 | Riwaz Garden | 0.58 | 0.41 | 0.37 | 0.04 | Lower |
15 | Muhammad | 0.31 | 0.52 | 0.56 | 0.04 | Lower |
16 | Dograi Kalan | 0.25 | 0.51 | 0.56 | 0.05 | Lower |
17 | Kareem Park | 0.55 | 0.43 | 0.38 | 0.05 | Lower |
18 | Minhala | 0.17 | 0.51 | 0.57 | 0.05 | Lower |
19 | Mian Meer | 0.27 | 0.48 | 0.43 | 0.06 | Lower |
20 | Hadiara | 0.34 | 0.49 | 0.55 | 0.06 | Lower |
21 | Shadman | 0.51 | 0.33 | 0.40 | 0.07 | Lower |
22 | Muhamad Colony | 0.62 | 0.39 | 0.33 | 0.07 | Lower |
23 | Mustafa Abad | 0.45 | 0.48 | 0.41 | 0.07 | Lower |
24 | Hanjarwal | 0.32 | 0.42 | 0.34 | 0.08 | Lower |
25 | Lakhodher | 0.27 | 0.49 | 0.57 | 0.08 | Lower |
26 | Chah Miran | 0.81 | 0.31 | 0.24 | 0.08 | Lower |
27 | Sodiwal | 0.84 | 0.38 | 0.46 | 0.08 | Lower |
28 | Maraka | 0.39 | 0.45 | 0.36 | 0.08 | Lower |
29 | Mozang | 0.75 | 0.44 | 0.36 | 0.09 | Lower |
30 | Salamat Pura | 0.90 | 0.54 | 0.45 | 0.09 | Lower |
31 | Sare Sultan | 0.83 | 0.41 | 0.32 | 0.09 | Lower |
32 | Gulgasht Colony | 0.83 | 0.38 | 0.47 | 0.09 | Lower |
33 | Sanda Khurd | 0.80 | 0.41 | 0.32 | 0.10 | Lower |
34 | Garhi Shahu | 0.52 | 0.40 | 0.50 | 0.10 | Lower |
35 | Shahpur | 0.38 | 0.45 | 0.35 | 0.10 | Lower |
36 | New Samanabad | 0.83 | 0.35 | 0.46 | 0.11 | Lower |
37 | Harbanspura | 0.64 | 0.47 | 0.36 | 0.11 | Lower |
38 | Shamke Bhattian | 0.01 | 0.50 | 0.39 | 0.11 | Lower |
39 | Bhaseen | 0.00 | 0.49 | 0.60 | 0.11 | Lower |
40 | Haloke | 0.78 | 0.44 | 0.33 | 0.11 | Lower |
41 | Jia Bagga | 0.38 | 0.46 | 0.35 | 0.11 | Lower |
42 | Bhaseen | 0.56 | 0.62 | 0.51 | 0.11 | Lower |
43 | Shah Kamal | 0.77 | 0.35 | 0.47 | 0.11 | Lower |
44 | Bilal Ganj | 0.76 | 0.43 | 0.32 | 0.11 | Lower |
45 | Ali Raza Abad | 0.75 | 0.45 | 0.33 | 0.12 | Low |
46 | Pandoki | 0.18 | 0.49 | 0.37 | 0.12 | Low |
47 | Kot Khawaja | 0.83 | 0.34 | 0.22 | 0.12 | Low |
48 | Chandrai | 0.68 | 0.44 | 0.32 | 0.12 | Low |
49 | Kot Lakhpat | 0.70 | 0.33 | 0.45 | 0.12 | Low |
50 | Hair | 0.26 | 0.49 | 0.36 | 0.13 | Low |
51 | Faiz Bagh | 0.84 | 0.34 | 0.21 | 0.13 | Low |
52 | Ganj Kalan | 0.83 | 0.43 | 0.30 | 0.13 | Low |
53 | Dhaloke | 0.25 | 0.49 | 0.35 | 0.13 | Low |
54 | Pakki Thatti | 0.81 | 0.32 | 0.46 | 0.14 | Low |
55 | Sabzazar | 0.46 | 0.48 | 0.34 | 0.14 | Low |
56 | Gajju Matta | 0.47 | 0.46 | 0.33 | 0.14 | Low |
57 | Khawaja Saeed | 0.47 | 0.50 | 0.36 | 0.14 | Low |
58 | Kasur Pura | 0.26 | 0.54 | 0.40 | 0.14 | Low |
59 | Sham Nagar | 0.81 | 0.32 | 0.46 | 0.14 | Low |
60 | Gulshan-e-ravi | 0.82 | 0.32 | 0.46 | 0.14 | Low |
61 | Race Course | 0.30 | 0.30 | 0.44 | 0.14 | Low |
62 | Manga | 0.19 | 0.53 | 0.38 | 0.14 | Low |
63 | Bibi Pak Daman | 0.65 | 0.32 | 0.47 | 0.15 | Low |
64 | Ichhra | 0.73 | 0.35 | 0.50 | 0.15 | Low |
65 | Islam Pura | 0.82 | 0.46 | 0.32 | 0.15 | Low |
66 | Kahna Nau | 0.31 | 0.49 | 0.34 | 0.15 | Low |
67 | Gawalmandi | 0.80 | 0.46 | 0.31 | 0.15 | Low |
68 | Niaz Beg | 0.34 | 0.50 | 0.35 | 0.15 | Low |
69 | Paji | 0.59 | 0.50 | 0.35 | 0.15 | Low |
70 | Johar Town | 0.63 | 0.48 | 0.33 | 0.15 | Low |
71 | Daras Baray Mian | 0.59 | 0.34 | 0.50 | 0.16 | Medium |
72 | Baghbanpura | 0.82 | 0.39 | 0.23 | 0.16 | Medium |
73 | Green Town | 0.73 | 0.47 | 0.30 | 0.16 | Medium |
74 | Kamahan | 0.64 | 0.49 | 0.32 | 0.17 | Medium |
75 | Rizwan Park | 0.75 | 0.32 | 0.49 | 0.17 | Medium |
76 | Dullo Khurd Kalan | 0.58 | 0.49 | 0.32 | 0.17 | Medium |
77 | Chung | 0.60 | 0.53 | 0.35 | 0.18 | Medium |
78 | Guldasht Colony | 0.67 | 0.52 | 0.34 | 0.18 | Medium |
79 | Fateh Garh | 0.88 | 0.61 | 0.29 | 0.18 | Medium |
80 | Shad Bagh | 0.86 | 0.42 | 0.24 | 0.19 | Medium |
81 | Faisal Town | 0.50 | 0.33 | 0.52 | 0.19 | Medium |
82 | Al-faisal Town | 0.80 | 0.50 | 0.31 | 0.19 | Medium |
83 | Rehman Pura | 0.82 | 0.27 | 0.46 | 0.19 | Medium |
84 | Sultan Mehmood | 0.79 | 0.62 | 0.43 | 0.19 | Medium |
85 | Gulberg | 0.46 | 0.30 | 0.49 | 0.19 | Medium |
86 | Township | 0.74 | 0.48 | 0.29 | 0.19 | Medium |
87 | Wassanpura | 0.83 | 0.43 | 0.23 | 0.20 | Medium |
88 | Farid Colony | 0.75 | 0.47 | 0.27 | 0.20 | Medium |
89 | Tajpura | 0.82 | 0.50 | 0.30 | 0.20 | Medium |
90 | Ameen Pura | 0.85 | 0.51 | 0.31 | 0.20 | Medium |
91 | Township Sec A | 0.70 | 0.51 | 0.31 | 0.20 | Medium |
92 | Bahawalpur Hs | 0.69 | 0.29 | 0.49 | 0.20 | Medium |
93 | Baghat Pura | 0.80 | 0.48 | 0.27 | 0.20 | Medium |
94 | Rehmat Pura | 0.83 | 0.45 | 0.25 | 0.21 | High |
95 | Nawan Kot | 0.79 | 0.27 | 0.48 | 0.21 | High |
96 | Taj Bagh | 0.72 | 0.52 | 0.31 | 0.21 | High |
97 | Gulshan-e-iqbal | 0.78 | 0.27 | 0.48 | 0.22 | High |
98 | Babu Sabu | 0.37 | 0.29 | 0.51 | 0.22 | High |
99 | Makkah Colony | 0.51 | 0.27 | 0.50 | 0.23 | High |
100 | Liaqatabad | 0.39 | 0.30 | 0.53 | 0.23 | High |
101 | Makhanpura | 0.83 | 0.45 | 0.22 | 0.23 | High |
102 | Begum Pura | 0.83 | 0.45 | 0.22 | 0.23 | High |
103 | Siddique Colony | 0.20 | 0.26 | 0.50 | 0.24 | High |
104 | Angori Bagh | 0.75 | 0.50 | 0.26 | 0.24 | High |
105 | Keer Kalan | 0.69 | 0.52 | 0.29 | 0.24 | High |
106 | Maryam Colony | 0.64 | 0.55 | 0.31 | 0.24 | High |
107 | Bhamman | 0.19 | 0.50 | 0.25 | 0.25 | High |
108 | Mujahidabad | 0.83 | 0.50 | 0.25 | 0.25 | High |
109 | Kashmir Block | 0.82 | 0.21 | 0.47 | 0.25 | High |
110 | Kot Begum | 0.45 | 0.50 | 0.24 | 0.26 | Higher |
111 | Crown Park | 0.81 | 0.50 | 0.23 | 0.27 | Higher |
112 | Bostan Colony | 0.79 | 0.56 | 0.29 | 0.27 | Higher |
113 | Samanabad | 0.68 | 0.24 | 0.51 | 0.27 | Higher |
114 | Nabipura | 0.84 | 0.58 | 0.31 | 0.27 | Higher |
115 | Ghaziabad | 0.78 | 0.58 | 0.30 | 0.28 | Higher |
116 | Awan Town | 0.82 | 0.56 | 0.28 | 0.28 | Higher |
117 | Ismail Nagar | 0.84 | 0.53 | 0.24 | 0.28 | Higher |
118 | Saidpur | 0.85 | 0.53 | 0.25 | 0.29 | Higher |
119 | Dhair | 0.10 | 0.58 | 0.29 | 0.29 | Higher |
120 | Zaman Park | 0.39 | 0.21 | 0.50 | 0.29 | Higher |
121 | Attari Saroba | 0.59 | 0.58 | 0.28 | 0.29 | Higher |
122 | Pindi Rajputan | 0.66 | 0.16 | 0.46 | 0.30 | Higher |
123 | Naseer Abad | 0.53 | 0.22 | 0.52 | 0.30 | Higher |
124 | Mughalpura | 0.78 | 0.61 | 0.30 | 0.30 | Higher |
125 | Al-hamra | 0.22 | 0.24 | 0.55 | 0.31 | Higher |
126 | Madhu Lal Husain | 0.83 | 0.53 | 0.22 | 0.31 | Higher |
127 | Muslim Town | 0.50 | 0.24 | 0.55 | 0.31 | Higher |
128 | Aziz Colony | 0.56 | 0.52 | 0.21 | 0.31 | Higher |
129 | Rashidpura | 0.85 | 0.61 | 0.29 | 0.32 | Higher |
130 | Fateh Garh | 0.83 | 0.47 | 0.29 | 0.32 | Higher |
131 | Siddiqia Colony | 0.54 | 0.56 | 0.24 | 0.32 | Higher |
132 | Faisal Park | 0.54 | 0.55 | 0.22 | 0.33 | Higher |
133 | Androon Texali | 0.67 | 0.56 | 0.23 | 0.33 | Higher |
134 | Bakar Mandi | 0.83 | 0.59 | 0.25 | 0.33 | Higher |
135 | Sikandar Block | 0.57 | 0.18 | 0.53 | 0.35 | Higher |
136 | Raiwind | 0.85 | 0.59 | 0.24 | 0.35 | Higher |
137 | Farooq Ganj | 1.00 | 0.50 | 0.15 | 0.35 | Higher |
138 | Androon Bhatti | 0.58 | 0.59 | 0.23 | 0.36 | Higher |
139 | Siddique Pura | 0.41 | 0.58 | 0.22 | 0.36 | Higher |
140 | Rang Mahal | 0.82 | 0.53 | 0.17 | 0.37 | Higher |
141 | Model Town | 0.39 | 0.16 | 0.53 | 0.37 | Higher |
142 | Garden Town | 0.44 | 0.14 | 0.51 | 0.38 | Higher |
143 | Androon Dehli | 0.67 | 0.59 | 0.21 | 0.38 | Higher |
144 | Shahdara | 0.47 | 0.60 | 0.22 | 0.38 | Higher |
145 | Qaiser Town | 0.27 | 0.63 | 0.25 | 0.38 | Higher |
146 | Jia Musa | 0.70 | 0.58 | 0.18 | 0.39 | Higher |
147 | Qila Lachhman | 0.74 | 0.58 | 0.18 | 0.41 | Higher |
148 | Bangali Bagh | 0.79 | 0.58 | 0.17 | 0.41 | Higher |
149 | Kot Mohibbu | 0.72 | 0.60 | 0.16 | 0.44 | Higher |
150 | Sittara Colony | 0.73 | 0.68 | 0.24 | 0.44 | Higher |
151 | Fruit Mandi | 0.58 | 0.64 | 0.19 | 0.45 | Higher |
Appendix D
References
- Satterthwaite, D. Chapter 8: Urban Areas from Climate Change 2014: Impacts, Adaptation, and Vulnerability. In Chapter from Climate Change 2014: Impacts, Adaptation, and Vulnerability; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2014. [Google Scholar]
- Paul, S.; Sanyaolu, O.; Urbanization. In Political Science and International Relations; Paul Sanyaolu’s Lab. 2018. Available online: https://books.google.co.jp/books?hl=zh-CN&lr=&id=QuTEDwAAQBAJ&oi=fnd&pg=PP7&dq=Political+Science+and+International+Relations&ots=4BIBDbdMcD&sig=rgGhYZmx1AJt-FcFHV0c-AScKLg&redir_esc=y#v=onepage&q=Political%20Science%20and%20International%20Relations&f=false (accessed on 20 August 2022).
- UN-Habitat. Urbanization and Development: Emerging Futures; UN-Habitat: Nairobi, Kenya, 2016; ISBN 978-92-1-132708-3. [Google Scholar]
- Pachauri, R.K.; Meyer, L.A. Climate Change 2014: Synthesis Report: Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; IPCC: Geneva, Switzerland, 2014; p. 151. [Google Scholar]
- IPCC. IPCC WGII Sixth Assessment Report: Summary for Policy Makers; IPCC: Geneva, Switzerland, 2022. [Google Scholar]
- Adeel, A.; Notteboom, B.; Yasar, A.; Scheerlinck, K.; Stevens, J. Insights into the Impacts of Mega Transport Infrastructures on the Transformation of Urban Fabric: Case of BRT Lahore. Sustainability 2021, 13, 7451. [Google Scholar] [CrossRef]
- Oke, T.R.; Mills, G.; Christen, A.; Voogt, J.A. Urban Climates; Cambridge University Press: Cambridge, UK, 2017; ISBN 9781139016476. [Google Scholar]
- Gensuo, J.; Shevliakova, E.; Land–climate interactions. In Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems; in press. 2019. Available online: https://www.ipcc.ch/site/assets/uploads/sites/4/2020/08/05_Chapter-2-V3.pdf (accessed on 20 August 2022).
- Brebbia, C.A.; Galiano-Garrigos, A. (Eds.) The Sustainable City XI. In Proceedings of the SUSTAINABLE CITY 2016, Alicante, Spain, 12–14 July 2016; WIT Press: Southampton, UK, 2016. [Google Scholar]
- Hamin, E.M.; Gurran, N. Urban form and climate change: Balancing adaptation and mitigation in the U.S. and Australia. Habitat Int. 2009, 33, 238–245. [Google Scholar] [CrossRef]
- Garschagen, M.; Romero-Lankao, P. Exploring the relationships between urbanization trends and climate change vulnerability. Clim. Chang. 2015, 133, 37–52. [Google Scholar] [CrossRef]
- Lavell, A.; Oppenheimer, M.; Diop, C.; Hess, J.; Lempert, R.; Li, J.; Muir-Wood, R.; Myeong, S.; Moser, S.; Takeuchi, K. Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience: A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC); Cambridge University Press: Cambridge, UK; New York, NY, USA, 2022; pp. 25–64. Available online: https://www.ipcc.ch/site/assets/uploads/2018/03/SREX-Chap1_FINAL-1.pdf (accessed on 20 May 2022).
- Birkmann, J.; Liwenga, E.; Pandey, R.; Boyd, E.; Djalante, R.; Gemenne, F. Poverty, Livelihoods and Sustainable Development: Climate Change 2022: Impacts, Adaptation and Vulnerability. In Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2022; Available online: https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_Chapter08.pdf (accessed on 9 August 2022).
- Kreibich, H.; van Loon, A.F.; Schröter, K.; Ward, P.J.; Mazzoleni, M.; Sairam, N.; Abeshu, G.W.; Agafonova, S.; AghaKouchak, A.; Aksoy, H.; et al. The challenge of unprecedented floods and droughts in risk management. Nature 2022, 608, 80–86. [Google Scholar] [CrossRef] [PubMed]
- Gencer, E.A. The Interplay between Urban Development, Vulnerability, and Risk Management: A Case Study of the Istanbul Metropolitan Area; Springer: Heidelberg, Germany; New York, NY, USA; Dordrecht, The Netherlands; London, UK, 2013; ISBN 978-3-642-29470-9. [Google Scholar]
- Fang, C.; Wang, Y.; Fang, J. A comprehensive assessment of urban vulnerability and its spatial differentiation in China. J. Geogr. Sci. 2016, 26, 153–170. [Google Scholar] [CrossRef]
- Solecki, W.; Seto, K.C.; Balk, D.; Bigio, A.; Boone, C.G.; Creutzig, F.; Fragkias, M.; Lwasa, S.; Marcotullio, P.; Romero-Lankao, P.; et al. A conceptual framework for an urban areas typology to integrate climate change mitigation and adaptation. Urban Clim. 2015, 14, 116–137. [Google Scholar] [CrossRef]
- Zhou, W.; Huang, G.; Cadenasso, M.L. Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landsc. Urban Plan. 2011, 102, 54–63. [Google Scholar] [CrossRef]
- Romero-Lankao, P.; Bulkeley, H.; Pelling, M.; Burch, S.; Gordon, D.J.; Gupta, J.; Johnson, C.; Kurian, P.; Lecavalier, E.; Simon, D.; et al. Urban transformative potential in a changing climate. Nat. Clim. Chang. 2018, 8, 754–756. [Google Scholar] [CrossRef]
- Jalalzadeh Fard, B.; Mahmood, R.; Hayes, M.; Rowe, C.; Abadi, A.M.; Shulski, M.; Medcalf, S.; Lookadoo, R.; Bell, J.E. Mapping Heat Vulnerability Index Based on Different Urbanization Levels in Nebraska, USA. Geohealth 2021, 5, e2021GH000478. [Google Scholar] [CrossRef]
- Koman, P.D.; Romo, F.; Swinton, P.; Mentz, G.B.; de Majo, R.F.; Sampson, N.R.; Battaglia, M.J.; Hill-Knott, K.; Williams, G.O.; O’Neill, M.S.; et al. MI-Environment: Geospatial patterns and inequality of relative heat stress vulnerability in Michigan. Health Place 2019, 60, 102228. [Google Scholar] [CrossRef]
- Burton, C.G. Social Vulnerability and Hurricane Impact Modeling. Nat. Hazards Rev. 2010, 11, 58–68. [Google Scholar] [CrossRef]
- Cutter, S.L.; Boruff, B.J.; Shirley, W.L. Social Vulnerability to Environmental Hazards. Soc. Sci. Q. 2003, 84, 242–261. [Google Scholar] [CrossRef]
- Fatemi, F.; Ardalan, A.; Aguirre, B.; Mansouri, N.; Mohammadfam, I. Social vulnerability indicators in disasters: Findings from a systematic review. Int. J. Disaster Risk Reduct. 2017, 22, 219–227. [Google Scholar] [CrossRef]
- Fekete, A. Validation of a social vulnerability index in context to river-floods in Germany. Nat. Hazards Earth Syst. Sci. 2009, 9, 393–403. [Google Scholar] [CrossRef]
- Handayani, W.; Rudiarto, I.; Setyono, J.S.; Chigbu, U.E.; Sukmawati, A.M. Vulnerability assessment: A comparison of three different city sizes in the coastal area of Central Java, Indonesia. Adv. Clim. Chang. Res. 2017, 8, 286–296. [Google Scholar] [CrossRef]
- Lee, W.; Choi, M.; Bell, M.L.; Kang, C.; Jang, J.; Song, I.; Kim, Y.-O.; Ebi, K.; Kim, H. Effects of urbanization on vulnerability to heat-related mortality in urban and rural areas in South Korea: A nationwide district-level time-series study. Int. J. Epidemiol. 2022, 51, 111–121. [Google Scholar] [CrossRef]
- Aksoy, E.; Gregor, M.; Fons, J.; Garzillo, C.; Cugny-Seguin, M.; Löhnertz, M.; Schröder, C. City typologies of Europe: A tool to support urban sustainability studies and practices. In The Sustainable City XI, Proceedings of the SUSTAINABLE CITY 2016, Alicante, Spain, 12–14 July 2016; Brebbia, C.A., Galiano-Garrigos, A., Eds.; WIT Press: Southampton, UK, 2016; pp. 199–210. [Google Scholar]
- Michalczyk, J. Urban Vulnerability Analysis towards Heat Based on the Example of the City Hanover; Institutionelles Repositorium der Leibniz Universität Hannover: Hannover, Germany, 2019; pp. 33–56. [Google Scholar]
- Heidari, H.; Arabi, M.; Warziniack, T.; Sharvelle, S. Effects of Urban Development Patterns on Municipal Water Shortage. Front. Water 2021, 3, 694817. [Google Scholar] [CrossRef]
- Tsilimigkas, G.; Deligianni, M.; Zerbopoulos, T. Spatial typologies of Greek coastal zones and unregulated Urban growth. J. Coast. Conserv. 2016, 20, 397–408. [Google Scholar] [CrossRef]
- Yang, J.; Gong, J.; Tang, W.; Liu, C. Patch-based cellular automata model of urban growth simulation: Integrating feedback between quantitative composition and spatial configuration. Comput. Environ. Urban Syst. 2020, 79, 101402. [Google Scholar] [CrossRef]
- Farkas, J.Z.; Hoyk, E.; Rakonczai, J. Geographical analysis of climate vulnerability at a regional scale: The case of the Southern Great Plain in Hungary. Hung. Geogr. Bull. 2017, 66, 129–144. [Google Scholar] [CrossRef] [Green Version]
- Soltanifard, H.; Aliabadi, K. Impact of urban spatial configuration on land surface temperature and urban heat islands: A case study of Mashhad, Iran. Theor. Appl. Climatol. 2019, 137, 2889–2903. [Google Scholar] [CrossRef]
- Jeddi Farzane, O.; Daryani, S.; Mokhberkia, M.M. Explanation of Urban Development Patterns in Order to Sustainable Development. J. Urban Manag. Energy Sustain. 2019, 2, 55–63. [Google Scholar] [CrossRef]
- Frenkel, A.; Ashkenazi, M. Measuring Urban Sprawl: How Can We Deal with It? Environ. Plan. B Plan. Des. 2008, 35, 56–79. [Google Scholar] [CrossRef]
- Krafta, R. Urban Convergence: Morphology and Attraction. Environ. Plan. B Plan. Des. 1996, 23, 37–48. [Google Scholar] [CrossRef]
- Yue, W.; Liu, X.; Zhou, Y.; Liu, Y. Impacts of urban configuration on urban heat island: An empirical study in China mega-cities. Sci. Total Environ. 2019, 671, 1036–1046. [Google Scholar] [CrossRef]
- Phinn, S.; Stanford, M.; Scarth, P.; Murray, A.T.; Shyy, P.T. Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques. Int. J. Remote Sens. 2002, 23, 4131–4153. [Google Scholar] [CrossRef]
- Rashed, T.; Weeks, R.; Roberts, D.; Rogan, J.; Powell, R. Measuring the Physical Composition of Urban Morphology Using Multiple Endmember Spectral Mixture Models. Photogramm. Eng. Remote Sens. 2003, 69, 1011–1020. [Google Scholar] [CrossRef]
- Sun, S.; Wang, Z.; Hu, C.; Gao, G. Understanding Climate Hazard Patterns and Urban Adaptation Measures in China. Sustainability 2021, 13, 13886. [Google Scholar] [CrossRef]
- Birkmann, J.; Cardona, O.D.; Carreño, M.L.; Barbat, A.H.; Pelling, M.; Schneiderbauer, S.; Kienberger, S.; Keiler, M.; Alexander, D.; Zeil, P.; et al. Framing vulnerability, risk and societal responses: The MOVE framework. Nat. Hazards 2013, 67, 193–211. [Google Scholar] [CrossRef]
- Ford, J.D.; Pearce, T.; McDowell, G.; Berrang-Ford, L.; Sayles, J.S.; Belfer, E. Vulnerability and its discontents: The past, present, and future of climate change vulnerability research. Clim. Chang. 2018, 151, 189–203. [Google Scholar] [CrossRef] [Green Version]
- Birkmann, J.; Jamshed, A.; McMillan, J.M.; Feldmeyer, D.; Totin, E.; Solecki, W.; Ibrahim, Z.Z.; Roberts, D.; Kerr, R.B.; Poertner, H.-O.; et al. Understanding human vulnerability to climate change: A global perspective on index validation for adaptation planning. Sci. Total Environ. 2022, 803, 150065. [Google Scholar] [CrossRef] [PubMed]
- Malakar, K.; Mishra, T. Assessing socio-economic vulnerability to climate change: A city-level index-based approach. Clim. Dev. 2017, 9, 348–363. [Google Scholar] [CrossRef]
- Yoon, D.K. Assessment of social vulnerability to natural disasters: A comparative study. Nat. Hazards 2012, 63, 823–843. [Google Scholar] [CrossRef]
- IPCC. Annex I: Glossary. In Global Warming of 1.5 °C; IPCC, Ed.; Cambridge University Press: Cambridge, UK, 2022; ISBN 9781009157940. [Google Scholar]
- Wisner, B.; Blaikie, P.; Cannon, T.; Davis, I. At Risk: Natural Hazards, People’s Vulnerability and Disasters, 2nd ed.; Routledge Taylor & Francis Group: London, UK, 2003. [Google Scholar]
- Jamshed, A.; Birkmann, J.; Feldmeyer, D.; Rana, I.A. A Conceptual Framework to Understand the Dynamics of Rural–Urban Linkages for Rural Flood Vulnerability. Sustainability 2020, 12, 2894. [Google Scholar] [CrossRef]
- Jamshed, A.; Rana, I.A.; Birkmann, J.; Nadeem, O. Changes in Vulnerability and Response Capacities of Rural Communities After Extreme Events: Case of Major Floods of 2010 and 2014 in Pakistan. J. Extrem. Events 2017, 4, 1750013. [Google Scholar] [CrossRef]
- Birkmann, J.; Mechler, R. Advancing climate adaptation and risk management. New insights, concepts and approaches: What have we learned from the SREX and the AR5 processes? Clim. Chang. 2015, 133, 1–6. [Google Scholar] [CrossRef]
- Birkmann, J.; Welle, T. Assessing the risk of loss and damage: Exposure, vulnerability and risk to climate-related hazards for different country classifications. Int. J. Glob. Warm. 2015, 8, 191. [Google Scholar] [CrossRef]
- Birkmann, J. (Ed.) Measuring Vulnerability to Natural Hazards: Towards Disaster Resilient Societies; United Nations University Press: Tokyo, Japan, 2006; ISBN 92-808-1135-5. [Google Scholar]
- Maharjan, S.K.; Maharjan, K.L.; Tiwari, U.; Sen, N.P. Participatory vulnerability assessment of climate vulnerabilities and impacts in Madi Valley of Chitwan district, Nepal. Cogent Food Agric. 2017, 3, 1310078. [Google Scholar] [CrossRef]
- Chrysoulakis, N.; Somarakis, G.; Stagakis, S.; Mitraka, Z.; Wong, M.-S.; Ho, H.-C. Monitoring and Evaluating Nature-Based Solutions Implementation in Urban Areas by Means of Earth Observation. Remote Sens. 2021, 13, 1503. [Google Scholar] [CrossRef]
- Feldmeyer, D.; Wilden, D.; Kind, C.; Kaiser, T.; Goldschmidt, R.; Diller, C.; Birkmann, J. Indicators for Monitoring Urban Climate Change Resilience and Adaptation. Sustainability 2019, 11, 2931. [Google Scholar] [CrossRef] [Green Version]
- Feldmeyer, D.; Birkmann, J.; Welle, T. Development of Human Vulnerability 2012–2017. J. Extrem. Events 2017, 4, 1850005. [Google Scholar] [CrossRef]
- Sorg, L.; Medina, N.; Feldmeyer, D.; Sanchez, A.; Vojinovic, Z.; Birkmann, J.; Marchese, A. Capturing the multifaceted phenomena of socioeconomic vulnerability. Nat. Hazards 2018, 92, 257–282. [Google Scholar] [CrossRef]
- Somarakis, G.; Stagakis, S.; Chrysoulakis, N. ThinkNature/Nature-Based Solutions Handbook; European Union. 2019. Available online: https://www.researchgate.net/publication/361888678_NATURE-BASED_SOLUTIONS_HANDBOOK (accessed on 20 August 2022).
- Stewart, I.D.; Oke, T.R. Local Climate Zones for Urban Temperature Studies. Bull. Am. Meteorol. Soc. 2012, 93, 1879–1900. [Google Scholar] [CrossRef]
- Wendnagel-Beck, A.; Ravan, M.; Iqbal, N.; Birkmann, J.; Somarakis, G.; Hertwig, D.; Chrysoulakis, N.; Grimmond, S. Characterizing Physical and Social Compositions of Cities to Inform Climate Adaptation: Case Studies in Germany. Urban Plan. 2021, 6, 321–337. [Google Scholar] [CrossRef]
- McGranahan, G.; Balk, D.; Anderson, B. The rising tide: Assessing the risks of climate change and human settlements in low elevation coastal zones. Environ. Urban. 2007, 19, 17–37. [Google Scholar] [CrossRef]
- de Sherbinin, A.; Schiller, A.; Pulsipher, A. The vulnerability of global cities to climate hazards. Environ. Urban. 2007, 19, 39–64. [Google Scholar] [CrossRef]
- Abebe, F.K. Modelling Informal Settlement Growth in Dar es Salaam, Tanzania; University of Twente: Enschede, The Netherlands, 2011. [Google Scholar]
- Wekesa, B.W.; Steyn, G.S.; Otieno, F. A review of physical and socio-economic characteristics and intervention approaches of informal settlements. Habitat Int. 2011, 35, 238–245. [Google Scholar] [CrossRef]
- Kisingo, A.; Muabsa, E.N. (Eds.) Impacts of Landuses on Diversity and Abundance of Avifauna in a Wetland: A Case of Lake Natron Basin. In Proceedings of the Sixth TAWIRI Scientific Conference, Arusha, Tanzania, 3–6 December 2007; Available online: https://www.researchgate.net/publication/299538108_Impacts_of_landuses_on_diversity_and_abundance_of_avifauna_in_a_wetland_A_case_of_Lake_Natron_basin (accessed on 20 August 2022).
- Government of Pakistan, Pakistan Demographic Survey, Ministry of Planning, Development and Special Initiatives, Pakistan Bureau of Statistics. Annu. Rep. 2022. Available online: https://www.pbs.gov.pk/publication/report-key-findings-pakistan-demographic-survey-2020 (accessed on 12 May 2022).
- Groote, P. Urban Planning in Lahore: A Confrontation with Real Development; Vakgroep Sociale en Economische Geografie: Groningen, The Netherlands, 1989; ISBN 903670183X. [Google Scholar]
- Rana, I.A.; Bhatti, S.S. Lahore, Pakistan—Urbanization challenges and opportunities. Cities 2018, 72, 348–355. [Google Scholar] [CrossRef]
- The Urban Unit. Punjab Cities Growth Atlas 1995–2015. 2018. Available online: https://urbanunit.gov.pk/UrbanAtlasCity/index.html#p=1 (accessed on 12 May 2022).
- Pakistan Meteorological Department. Annual Report 2020. Available online: https://www.pmd.gov.pk/meteorogram/punjab.php?district=Lahore&division=Lahore (accessed on 13 May 2022).
- CNN. Climate Change Is Making Record Heatwaves in India and Pakistan 100 Times More Likely. Available online: https://edition.cnn.com/2022/05/18/asia/climate-india-pakistan-heatwave-intl/index.html (accessed on 30 May 2022).
- Justin Rowlatt. Climate Change Swells Odds of Record India, Pakistan Heatwaves. BBC News, 18 May 2022. Available online: https://www.bbc.com/news/science-environment-61484697 (accessed on 30 May 2022).
- Zuhra, S.S.; Tabinda, A.B.; Yasar, A. Appraisal of the heat vulnerability index in Punjab: A case study of spatial pattern for exposure, sensitivity, and adaptive capacity in megacity Lahore, Pakistan. Int. J. Biometeorol. 2019, 63, 1669–1682. [Google Scholar] [CrossRef]
- The Guardian. We Are Living in Hell: Pakistan and India Suffer Extreme Spring Heatwaves. The Guardian, 5 February 2022. Available online: https://www.theguardian.com/world/2022/may/02/pakistan-india-heatwaves-water-electricity-shortages (accessed on 30 May 2022).
- Jiao, J.; Rollo, J.; Fu, B. The Hidden Characteristics of Land-Use Mix Indices: An Overview and Validity Analysis Based on the Land Use in Melbourne, Australia. Sustainability 2021, 13, 1898. [Google Scholar] [CrossRef]
- Song, Y.; Merlin, L.; Rodriguez, D. Comparing measures of urban land use mix. Comput. Environ. Urban Syst. 2013, 42, 1–13. [Google Scholar] [CrossRef]
- Im, H.N.; Choi, C.G. The hidden side of the entropy-based land-use mix index: Clarifying the relationship between pedestrian volume and land-use mix. Urban Stud. 2019, 56, 1865–1881. [Google Scholar] [CrossRef]
- Turner, M.G.; Gardner, R.H. Landscape Ecology in Theory and Practice; Springer: New York, NY, USA, 2015; ISBN 978-1-4939-2793-7. [Google Scholar]
- Yang, X.; Liu, S.; Jia, C.; Liu, Y.; Yu, C. Vulnerability assessment and management planning for the ecological environment in urban wetlands. J. Environ. Manag. 2021, 298, 113540. [Google Scholar] [CrossRef] [PubMed]
- Sandholz, S.; Sett, D.; Greco, A.; Wannewitz, M.; Garschagen, M. Rethinking urban heat stress: Assessing risk and adaptation options across socioeconomic groups in Bonn, Germany. Urban Clim. 2021, 37, 100857. [Google Scholar] [CrossRef]
- Conlon, K.C.; Mallen, E.; Gronlund, C.J.; Berrocal, V.J.; Larsen, L.; O’Neill, M.S. Mapping Human Vulnerability to Extreme Heat: A Critical Assessment of Heat Vulnerability Indices Created Using Principal Components Analysis. Environ. Health Perspect. 2020, 128, 97001. [Google Scholar] [CrossRef]
- Xu, L.; Cui, S.; Tang, J.; Nguyen, M.; Liu, J.; Zhao, Y. Assessing the adaptive capacity of urban form to climate stress: A case study on an urban heat island. Environ. Res. Lett. 2019, 14, 44013. [Google Scholar] [CrossRef]
- Li, Y.; Schubert, S.; Kropp, J.P.; Rybski, D. On the influence of density and morphology on the Urban Heat Island intensity. Nat. Commun. 2020, 11, 2647. [Google Scholar] [CrossRef]
- Yang, X.S. Nature-Inspired Optimization Algorithms; Elsevier: Amsterdam, The Netherlands, 2014; ISBN 9780124167438. [Google Scholar]
- Spielman, S.E.; Tuccillo, J.; Folch, D.C.; Schweikert, A.; Davies, R.; Wood, N.; Tate, E. Evaluating social vulnerability indicators: Criteria and their application to the Social Vulnerability Index. Nat. Hazards 2020, 100, 417–436. [Google Scholar] [CrossRef]
- Zhou, B.; Rybski, D.; Kropp, J.P. The role of city size and urban form in the surface urban heat island. Sci. Rep. 2017, 7, 4791. [Google Scholar] [CrossRef]
- Bek, M.A.; Azmy, N.; Elkafrawy, S. The effect of undeveloped growth of urban areas on heat island phenomena. Ain Shams Eng. J. 2018, 9, 3169–3177. [Google Scholar] [CrossRef]
- Mills, E. Insurance in a climate of change. Science 2005, 309, 1040–1044. [Google Scholar] [CrossRef]
- Yang, X.-S. Multi-Objective Optimization. In Nature-Inspired Optimization Algorithms; Elsevier: Amsterdam, The Netherlands, 2014; pp. 197–211. [Google Scholar]
- Wang, Y.; Li, X.; Li, J.; Huang, Z.; Xiao, R. Impact of Rapid Urbanization on Vulnerability of Land System from Complex Networks View: A Methodological Approach. Complexity 2018, 2018, 1–18. [Google Scholar] [CrossRef]
- Pouriyeh, A.; Lotfi, F.H.; Pirasteh, S. Vulnerability Assessment and Modelling of Urban Growth Using Data Envelopment Analysis. J. Indian Soc. Remote Sens. 2021, 49, 259–273. [Google Scholar] [CrossRef]
- Rinner, C.; Patychuk, D.; Bassil, K.; Nasr, S.; Gower, S.; Campbell, M. The Role of Maps in Neighborhood-level Heat Vulnerability Assessment for the City of Toronto. Cartogr. Geogr. Inf. Sci. 2010, 37, 31–44. [Google Scholar] [CrossRef]
- Kim, S.; Ryu, Y. Describing the spatial patterns of heat vulnerability from urban design perspectives. Int. J. Sustain. Dev. World Ecol. 2015, 22, 189–200. [Google Scholar] [CrossRef]
- Hassanien Al-Sayed, S. The role of strategic planning in Spatial Competing between developed and undeveloped urban areas (Case study: Urban Areas of Greater Cairo). JES. J. Eng. Sci. 2021, 49, 850–870. [Google Scholar] [CrossRef]
- Mahtta, R.; Mahendra, A.; Seto, K.C. Building up or spreading out? Typologies of urban growth across 478 cities of 1 million+. Environ. Res. Lett. 2019, 14, 124077. [Google Scholar] [CrossRef]
- Ribeiro, F.L. Undeveloped Urban Development: A Neglected Global Threat. Curr. Urban Stud. 2021, 9, 434–444. [Google Scholar] [CrossRef]
- Salas, J.; Yepes, V. Urban vulnerability assessment: Advances from the strategic planning outlook. J. Clean. Prod. 2018, 179, 544–558. [Google Scholar] [CrossRef]
Indicator | Source | Explanation and Relevant Studies | Unit | Transformation Method |
---|---|---|---|---|
Susceptibility | ||||
Age group < 5 and >65 | Population census | People aged between 0 to 4 years and above 65 years is an important vulnerability determinant concerning weather-related stresses [20,29,81]. | Inhabitants | Min–max normalization |
Pre-existing illness | Rolling plan of the health department | Populations with existing health issues are more susceptible to heat stress [74,82,84]. | Percentage | Scaled 0 to 1 |
Household Income | Lahore Urban Transport Master Plan | Relative poverty causes social exclusion and increases an individual’s susceptibility [85] | Average | Min–max normalization |
Education level | Multiple indicators cluster survey (MICS) | A lower education level closely correlates with the individual’s ability to comprehend climate hazards [20,74,86]. | Percentage | Scaled 0 to 1 |
Household size | MICS | The larger the family size, the more people are vulnerable to heat stress [20,74]. | Percentage | Scaled 0 to 1 |
Coping and adaptive capacity | ||||
Ownership status | MICS | People who own a house and other assets have more ability to adapt [87,88]. | Percentage | Scaled 0 to 1 |
Access to hospitals | MICS | Access to healthcare is another factor that determines the copying capacity of individuals to heat events [20]. | Percentage | Scaled 0 to 1 |
Access to green | Landsat 8 | The density of green spaces on a patch of land is important to describe the capacity of people that can withstand extreme heating events [29,74] | −1 to +1 | Min–max normalization |
Access to water | Landsat 8 | A water body tends to have low radiation and strong absorption; therefore, it significantly reduces the heat stress [20,29]. | −1 to +1 | Min–max normalization |
Access to electricity | MICS | The access to electricity can increase an individual’s capacity to cope with heat stress [20,86]. | Percentage | Scaled 0 to 1 |
Access to an improved water source | MICS | The access to improved water supply can be helpful to minimize the effects of heat-related events [20,29,86]. | Percentage | Scaled 0 to 1 |
Access to information | MICS | The access of households to electronic or print media is an important determinant while quantifying heat-relevant coping capacity [34,81]. | Percentage | Scaled 0 to 1 |
Use of air conditioning | MICS | Air conditioning is a spontaneous coping-related measure for rising temperatures [27,38]. | Percentage | Scaled 0 to 1 |
Insurance coverage | MICS | By dealing with the effects of heat stress, insurance coverages can provide financial security [86,89]. | Percentage | Scaled 0 to 1 |
Dependent Variable: Human Heat Vulnerability | |||||
---|---|---|---|---|---|
Source | Sum of Squares | Degree of Freedom (df) | Mean Square | F | Significance (p) |
Corrected model | 3.375 a. | 22 | 0.153 | 3.565 | <0.001 |
Intercept | 23.676 | 1 | 23.676 | 550.196 | <0.001 |
Settlement area type | 1.535 | 2 | 0.768 | 17.841 | <0.001 |
Building density | 0.112 | 2 | 0.056 | 1.296 | 0.277 |
ENT | 0.492 | 2 | 0.246 | 5.721 | 0.004 |
Settlement area type * Building density | 0.487 | 3 | 0.162 | 3.771 | 0.012 |
Settlement type * ENT | 0.275 | 4 | 0.069 | 1.596 | 0.179 |
Building density * ENT | 0.120 | 4 | 0.030 | 0.696 | 0.596 |
Settlement area type * Building density * ENT | 0.260 | 5 | 0.052 | 1.209 | 0.309 |
Error | 5.508 | 128 | 0.043 | ||
Total | 56.870 | 152 | |||
Corrected total | 8.883 | 150 |
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Iqbal, N.; Ravan, M.; Jamshed, A.; Birkmann, J.; Somarakis, G.; Mitraka, Z.; Chrysoulakis, N. Linkages between Typologies of Existing Urban Development Patterns and Human Vulnerability to Heat Stress in Lahore. Sustainability 2022, 14, 10561. https://doi.org/10.3390/su141710561
Iqbal N, Ravan M, Jamshed A, Birkmann J, Somarakis G, Mitraka Z, Chrysoulakis N. Linkages between Typologies of Existing Urban Development Patterns and Human Vulnerability to Heat Stress in Lahore. Sustainability. 2022; 14(17):10561. https://doi.org/10.3390/su141710561
Chicago/Turabian StyleIqbal, Nimra, Marvin Ravan, Ali Jamshed, Joern Birkmann, Giorgos Somarakis, Zina Mitraka, and Nektarios Chrysoulakis. 2022. "Linkages between Typologies of Existing Urban Development Patterns and Human Vulnerability to Heat Stress in Lahore" Sustainability 14, no. 17: 10561. https://doi.org/10.3390/su141710561