Determining the components of physical resilience in the residential texture of Bojnourd City using Moran’s I spatial autocorrelation

Subject Areas :

Yaghob Abdali ¹ , Seyd Abas Rajaei ²

1 - Ph.D. student of Geography and Urban Planning, Faculty of Geography, University of Tehran
2 - Assistant Professor of Geography and Urban Planning, University of Tehran

Received: 2018-02-23 Accepted : 2019-10-18 Published : 2020-01-21

Keywords: Resilience, Physical, ALM, residential, Bojnourd,

Abstract :

Today, cities and residential societies are mainly established where they are subject to different types of natural disasters or are subject to different man-made disasters due to technological advancements. Hence, one of the most important requirements is addressing the physical issues of urban residential textures and the concept of resilience and consequently, retrofitting these textures in case of possible incidents and hazards. The currently studied domain was the residential texture of Bojnourd County with a total population of 233810, with 34557 residential buildings and a total area of 7859902 m². This study is an applied research in terms of objective and in terms of methodology, it is a descriptive-analytical research based on physical-structural approaches. In order to obtain the objectives of this research, selected indices such as the structural skeleton, materials, the number of floors, gradation, building age, and the quality of buildings were extracted. In order to determine the trends of the patterns, the regression tool was used. Also, for weighting of the layers in the spatial data, the spatial autocorrelation tool of Weights Manager, available in Geoda software, is utilized. Moran’s I tool is used for determining the type of physical resilience distribution (clustered, random, and sparse). Furthermore, Anselin Local Morans spatial autocorrelation tool in ArcGIS software was used for studying the spatial distribution of the residential texture resilience prioritization. The spatial results in the current study shows that 2235783 m² i.e. 28.45% of the total area including 10187 residential blocks are in the relatively low to very low resilience range and 3513732 m² i.e. 44.70 of the total area including 15269 residential blocks are in the medium resilience range which require immediate planning. Finally, based on the findings of this research and within the framework of the six selected components, suggestions are made on the physical resilience of the residential texture of Bojnourd County.
 
Key words: Resilience, physical, residential, ALM, Bojnourd.
 



 


 
 



 
Extended Abstract
Introduction:
      One of the problems that have always threatened the lives of human societies over many centuries is the occurrence of disasters and disasters that, if unaware and unprepared, cause irreparable damage to various aspects of human life, including social, economic, Imports the environment, psychology, etc. Today, due to the rapid change of cities, part of the urban fabric due to inefficiency have failed to establish a proper relationship with their environment and service to the users. In the meantime, threats such as the existence of hazards, the mismatch of tissues with present-day needs, as well as the wear and tear of these urban tissues, and in particular residential tissues, have been threatened, hence one of the most important requirements for addressing the physical problems of tissues. Residential in cities and explaining the concept of resilience and consequently the resilience of these tissues in the face of potential accidents and hazards. Today, there is a dramatic shift in attitude towards residential contexts worldwide, with the dominant focus being on reducing vulnerability to increasing resilience to any risk. This paradigm with multidisciplinary approach and twist planning It utilizes ecosystems to increase the capacity of ecological systems to absorb disturbances and to maintain the necessary feedback, processes, and inherent structures of the system.The main problem of cities in Iran, which has always driven urban planners and officials, is the physical decline and problematic urban texture or poor texture in the urban space and the adverse effects of non-compliance with resilience principles in urban areas. This field can be upgraded. Bojnourd, as an emerging metropolis, suffers from undesirable textures, influenced by development factors such as the establishment of industries, particularly heavy industries, and the acceptance of immigrant floods, as well as the temporal incompatibility of urban textures with existing needs. Considering the special status of Bojnourd metropolis and its importance in social and cultural dimensions among the cities of eastern Iran and its highs and lows and its importance at national level, its study as a city has a history of resilience. It is very important.
Methodology:
        This study is an applied research in terms of objective and in terms of methodology, it is a descriptive-analytical research based on physical-structural approaches. In order to obtain the objectives of this research, selected indices such as the structural skeleton, materials, the number of floors, gradation, building age, and the quality of buildings were extracted. In order to determine the trends of the patterns, the regression tool was used. Also, for weighting of the layers in the spatial data, the spatial autocorrelation tool of Weights Manager, available in Geoda software, is utilized. Moran’s I tool is used for determining the type of physical resilience distribution (clustered, random, and sparse). Furthermore, Anselin Local Morans spatial autocorrelation tool in ArcGIS software was used for studying the spatial distribution of the residential texture resilience prioritization.
Results and discussion:
            To analyze the map of the resilience indices, after determining the status quo and categorizing each index based on the degree of resilience (high resilience to non-reflux), vectors were presented based on spatial representation. Examination of the building skeleton in the study area shows that about 40% have no skeleton, 31% steel and 28% concrete. Examination of the number of building floors in the study shows that of the existing buildings, about 74% are single storeys, 20% are two storeys, about 4% are three floors, and about 2% are four and more than four floors. The quality of the buildings has been evaluated based on five criteria: new, acceptable, repairs, demolition and lack of quality; Relatively high-rise buildings in this area, especially in the central areas of tissue, have complied with the lack of standards in recent construction due to the inability of residents to settle and mainly in the marginal areas. Also the criteria for renovating, accepting, demolishing and lacking quality include 31.54%, 28.27% and 4.54% of the existing buildings in the study area, respectively. In this paper, the size of the disaggregated fragments, the area of the studied fragments are classified into four classes of less than 100 m 2, 100 m 2 to 200 m 2, 200 m to 300 m 2 and more than 300 m 2, each of which 2.99%, 28.36%, 32.84% and 35.80% of the study area were included. The status of the materials used in the buildings under study was 31.54%, brick and cement 28.27%, cement block 35.69% and brick 4.58%, respectively. Surveying the life expectancy and age of existing buildings in the study area shows about 40% more than 30 years, 31% less than 10 years, 28% between 20 and 30 years, and less than 1% between 10 and 20 years.
Conclusion:
          In the present study, after extraction of physical resilience indices of residential texture in Bojnourd, the regression tool was used to detect pattern trends, and spatial autocorrelation method was used to weight the layers in spatial data. Moran's I tool was used to determine the physical resilience distribution of residential texture (cluster, random, and dispersed). Also, the spatial distribution of resilience indices in residential texture was used by the Anselin Local Morans method and texture preferences were extracted with respect to the existing spatial and spatial relationships and priorities were determined at very high to very low resilience levels. Physical resilience of residential texture of Bojnourd city was categorized into five spectra, among which the average resilience with the highest percentage (70/44 which covers 3513732 sqm of residential texture of Bojnourd) was 1763787 sqm or 22.44% of the total area. Residential texture in very low resiliency which needs to be programmed for these parts of the tissue as soon as possible, 1701504 m 2 (21.65%) Residential texture in high resiliency, 471996 m 2 (6.1%) Low yields and 408883sq m (5.20%) are in high recovery condition.

References:

Abdali, Y., hataminejad, H., Pourahmad, A & Ziyari, K (2019). Analysis and Evaluation of the Vulnerable and Unstable Textile Vulnerability to Earthquake. Geographic Exploration of Desert Areas, 7(1), 41- 63.

Adger, W. N., Hughes, T. P., Folke, C., Carpenter, S. R., & Rockström, J. (2005). Social-ecological resilience to coastal disasters. Science, 309(5737), 1036-1039.‏

Alberti, M. (2005). The effects of urban patterns on ecosystem function. International regional science review, 28(2), 168-192.‏‏

Alizadeh, M (2015). Vulnerability Assessment of Kouhdasht Urban Infrastructure with Passive Defense Approach. M.Sc., Assistant Professor Saeed Amanpour, Department of Geography and Urban Planning, Shahid Chamran University of Ahvaz.

Andalib, A (2009). Regeneration process of worn out tissues of Tehran. Ray Poor Publishing, Tehran.

Bromley, R. D., Tallon, A. R., & Thomas, C. J. (2005). City centre regeneration through residential development: Contributing to sustainability. Urban Studies, 42(13), 2407-2429.‏

ESRI, (2016). An Overview of the Spatial Statistics Toolbox. ArcGIS 10.5 Online HelpSystem (ArcGIS 10.5 Desktop, Release 10.5, 2016). Environmental Systems Research Institute, Redlands, CA.

Folke, C., Carpenter, S., Walker, B., Scheffer, M., Elmqvist, T., Gunderson, L., & Holling, C. S. (2004). Regime shifts, resilience, and biodiversity in ecosystem management. Annual Review of Ecology, Evolution, and Systematics, 35.

Goovaerts, P., Jacquez, G.M., (2004). Accounting for regional background and populationsize in the detection of spatial clusters and outliers using geostatisticalfiltering and spatial neutral models: the case of lung cancer in Long Island, NewYork. Int. J. Health Geogr. 3, 14.

Habibi, K., Behzadfar, M., Meshkini, A & Nazari, S (2014). Development of a model for predicting the instability of ancient urban textures against earthquakes with Inverted Hierarchical Logic (IHPW) and Geographic Information System (GIS). Journal of Earth Sciences, 22(87), 83- 92.

Habibi, K., Pourahmad, A & Meshkini, A (2013). Improvement and modernization of old urban textures. Entekhab Publication, Tehran.

Hataminejad, H., Abdali, Y & Allahgholipour, S (2018). Assessment of Structural Vulnerability of Urban Deteriorated Textures Against Hazards with Passive Defense Approach (Case Study: Central Deteriorated Texture of Ahvaz Metropolis). Geographical Information Journal (Sepehr), 26(104), 159- 172.

Havko, J., Titko, M., & Kováčová, J. (2017). Vulnerability of the city infrastructure as a part of the resilient city concept. Procedia engineering, 192, 307-312.‏

Holling,C.S., L.H.,Gunderson .(2002). Resilience and adaptive cycles. In: L H Gunderson and C SHolling (editors). Panarchy: Understanding Transformations in Human and Natural Systems.

Iran Statistics Center (2016). Results of Census 2016.

Jacquez, G.M., Greiling, D.A., (2003a). Local clustering in breast, lung and colorectalcancer in Long Island, New York. Int. J. Health Geogr. 2, 3.

Jacquez, G.M., Greiling, D.A., (2003b). Geographic boundaries in breast, lung andcolorectal cancer in relation to exposure to air toxics in Long Island, New York. Int. J. Health Geogr. 2, 4.

Mayunga, J. S. (2007). Understanding and applying the concept of community disaster resilience: a capital-based approach. Summer academy for social vulnerability and resilience building, 1, 16.‏

Mitchell, T., & Harris, K. (2012). Resilience: A risk management approach. ODI Background Note. Overseas Development Institute: London.‏

Mohamadi, P (2018). Earthquake Resilience Study (Case Study: Area 1, District 9, Tehran). M.Sc., Hossein Hataminejad, Department of Geography and Urban Planning, University of Tehran.

Nouri, M., Mohammadi, J & Mohammadi, J (2016). Analysis of Spatial-Physical Structure of Urban Decay Texture with Renovation and Improvement Approach (Case Study: DoGonbandan Urban Decay Texture). Spatial Planning Quarterly 4 (2), 105- 128.

Patrick, B. C., & Michael, P. B. (2018). Towards resilient cities in Ghana: Urban planners’ perspective and strategies. Futures.‏

Pourahmad, A., Abdali, Y., Sadeghi, A & Allahgholipour, S (2018). Spatial Analysis and Analysis of Physical Resilience Components in the Central Context of Hamadan City Using Moran Spatial Autocorrelation. Physical Development Planning Quarterly, 3(9), 92- 104.

Sadeghi, A (2018). Spatial Analysis of Physical Resilience of Urban Decay Texture with Crisis Management Approach: A Case Study: District 10 of Tehran. Master's thesis, Guide Ahmad Pour Ahmad, Department of Geography and Urban Planning, University of Tehran.

UN/ISDR. (2002). Disaster reduction and sustainable developement: understanding the links between vulnerability and risks to disasters related to developement and environment. World Summit on Sustainable Developement (Johannesburg) 24 pp.

Zamiri, M., Zamiri, M & Nastaran, M (2014). Quantitative Methods in Analyzing the Spatial Development of Urban Housing in Bojnourd (2005-2005). Journal of Urban Studies, 5(17), 67- 76.

Zhang, H., & Tripathi, N. K. (2018). Geospatial hot spot analysis of lung cancer patients correlated to fine particulate matter (PM2. 5) and industrial wind in Eastern Thailand. Journal of Cleaner Production, 170, 407-424.‏