Mapping Earthquake Risk Sensitivity of Land Use Plan at Local Level for Sustainable Risk Sensitive Land Use Planning (RSLUP)

Risk-Sensitive Land Use Planning (RSLUP) is the process of mainstreaming disaster risk management parameters in land use planning. To ensure the effectiveness and sustainability of RSLUP, it is necessary to identify and understand the existing risk sensitivity of the land use plan. This research aims to develop a GIS-based multi-criteria zoning approach for mapping earthquake risk sensitivity of the land use plan of a local level area. For this purpose, Uttara Residential Model Town (URMT) (third phase), Dhaka, Bangladesh has been selected as the study area considering its earthquake risk for exposure to a potential earthquake. The methodology applied in this research is comprised of two steps. Firstly, assessment of the spatial earthquake risk sensitivity of the proposed land use plan of the study area based on the risk themes and corresponding risk attributes including both natural characteristics as well as built environment factors. They are macro-form risks (seismic hazard assessment), risks in urban texture (proximity from primary roads), special risk areas (geomorphic suitability and proximity from waterbody), open space scarcity risk, and risks in critical facilities (potential temporary disaster shelter and health facilities). Secondly, preparation of earthquake risk sensitivity zoning map by overlaying the spatial risk attribute maps based on weights determined through Analytical Hierarchical Process (AHP). This research brings out the importance and a methodology to assess risk sensitivity of the land use of an area at the local level, which can further foster sustainable RSLUP reecting the risk sensitivity accordingly and effectively.

planning. Several initiatives have been taken by the Government of the Republic of Bangladesh (GoB) for integrating risk sensitivity in the land use planning process (UDD, 2011;UDD & ADPC, 2013. Nevertheless, none of the researches focused on the assessment of earthquake risk sensitivity of the land use plan at the local level before the RSLUP. The third phase of Uttara Residential Model Town (URMT) is one of the township projects initiated by the Capital Development Authority (RAJUK), which is under development process at present (RAJUK, 2016). It is located in the northern part of the Dhaka Metropolitan area, which has been identi ed as a highly earthquake-prone area (EMI, 2014). Considering these conditions, the third phase of URMT was selected as the study area for this research. Figure 1 shows a satellite image of the study area (URMT 3rd phase) along with its location in Dhaka city in the inset.

Data Collection and Processing
Necessary data required for this research were collected from secondary sources. Land use map of the study area (URMT 3rd phase) was collected from RAJUK (RAJUK, 2010(RAJUK, , 2016. Borehole test data for the study area were collected from the Department of Civil Engineering, Bangladesh University of Engineering and Technology (BUET). The GIS shape les of geomorphology and suitability of structures considering the geomorphic unit of the Dhaka Metropolitan area were collected from the Geological Survey of Bangladesh (GSB) (GSB, 2016). From these collected resources, data of the study area were extracted for this research.

Assessment of Earthquake Risk Sensitivity of the Land Use Plan
For effective and sustainable RSLUP, rstly it is necessary to assess its risk sensitivity (Burby et al., 2000;WBI, 2006cWBI, , 2006d. In this research, the assessment was done based on some risk sectors or risk themes. Risk sectors or themes are distinctly manageable clusters of probabilities and vulnerabilities for which a coordinated approach action is necessary through exploration of risk factors in each sector (WBI, 2006e). World Bank and EMI (2014) proposed some risk themes and their corresponding risk factors in the Risk-Sensitive Land Use Planning Guidebook for Dhaka city under Bangladesh Urban Earthquake Resilience Project. Considering the context of this research and the status of development of the study area, six of the risk themes were selected as the criteria for the earthquake risk sensitivity assessment of the land use plan of the study area in this research. These including both natural characteristics as well as built environment factors. Although this research considered the World Bank and EMI (2014) for the selection of the risk themes as the basis of the risk assessment, this document just identi ed the risk themes to be considered for RSLUP. However, this document does not propose theme-wise methodology for the assessment of earthquake risk sensitivity of the land use plan. Nor does it propose anything regarding aggregation of the ndings. Therefore, for utilizing the selected risk themes for risk sensitivity assessment in this research, it was necessary to further de ne these risk themes adjusting with the local context, to translate them into the spatial aspect, and to develop a methodology to aggregate them to get a cumulative multi-criteria risk sensitivity-zoning map. For spatial translation, the risk attributes were classi ed into risk sensitivity scales (1 to 5 where 1 represents low-risk sensitivity and 5 represents high-risk sensitivity) based on the review of different works of literature. Table 1 shows risk themes and their corresponding spatial risk attributes as well as risk sensitivity scales. Source: Adapted from (Argo & Sandstrom, 2014;Brender, Maantay, & Chakraborty, 2011;Çabuk, 2001;MfE New Zealand, 2002;UDD & ADPC, 2016)

Seismic Hazard Assessment of the Study Area
Among the risk themes shown in Table 1, macro form risks include understanding and evaluation of the earthquake hazard in an area to ensure increased resiliency through successful adoption of appropriate measures in land use planning (Erdik, 2006;Guragain, Jimee, & Dixit, 2008;Villacis, 2000). It helps to identify and avoid potential problems associated with development in hazard-prone areas (Burby et al., 2000;Çabuk, 2001). In this research, for assessment of earthquake hazard in the study area, rstly scenario earthquake was selected based on the study of the GoB on earthquake scenarios for Dhaka city under Comprehensive Disaster Management Program (CDMP) (CDMP, 2009). The detailed discussion and ndings from scenario selection have been discussed in the Section 4.1. Based on the selected scenario earthquake, earthquake hazard in the study area was assessed through assessment of ground shaking, ground response or soil ampli cation, and soil liquefaction susceptibility. After that, the seismic hazard map of the study area was prepared. It enables the identi cation of earthquake-prone areas at a local scale to undertake risk reduction and management strategies (WBI, 2006d). The assessment steps and methods are discussed in the following sections:

Assessment of Ground Shaking
An earthquake with one magnitude and one epicenter produces a range of ground shaking levels at sites throughout the region depending on the distance from the epicenter. It is represented in terms of Peak Ground Acceleration (PGA), which is the peak horizontal acceleration as a percent of acceleration due to the force of gravity or "g". After assessment of ground shaking due to an earthquake in an area, it is represented by a "shakemap" which is a representation of ground shaking distribution in an area produced by an earthquake (USGS, 2011). There are several modeling tools available for the assessment of ground shaking. They are HAZUS (HAZards United States), GIS-based RADIUS (Risk Assessment tools for Diagnosis of Urban areas against Seismic disasters), SELENA (SEismic Loss EstimatioN using a logic tree Approach), ShakeMap, KMH (Karmania hazard model), etc. Table 2 shows a comparison among the ground shaking assessment methods.  (2013) Among these methods, GIS-based RADIUS is more convenient to use for its simple methodology and it does not require detail technical knowledge on earthquake engineering (Guragain et al., 2008). Though it is not su cient in terms of detailing, it is good enough for decision making, considering the nancial and temporal aspects (Alam, Tesfamariam, & Alam, 2012). Some studies in Bangladesh also utilized the RADIUS tool (S. Ahmed, Urmi, Islam, Alam, & Munna, 2015;Senjuti & Islam, 2014). Thus, to prepare the ground shaking map of the study area in terms of Peak Ground Acceleration (PGA) and intensity, the RADIUS tool was used in this research.
For this purpose, rstly the study area was divided into square grids with a dimension of 0.25 km by 0.25 km using the Fishnet tool in ArcGIS 10.3.1 considering the dimension of the study area. After that, the grids were inputted in the excel-based RADIUS tool. Then, the attributes of the selected scenario earthquake (including coordinate of the epicenter, the magnitude of the earthquake, depth to the top of fault in km, and distance of the epicenter from the study area) were inputted. After running the tool using inputted data, shake maps of the study area were produced in terms of PGA and intensity. After that, the grid-wise values of ground shaking in terms of PGA and intensity were inputted in ArcGIS 10.3.1. Thus, the spatial distribution of ground shaking attributes (PGA and intensity) in the study area was computed for scenario earthquakes.

Assessment of Ground Response or Soil Ampli cation
Ground shaking is the primary cause of earthquake damage to man-made structures, but at such events, someplace in the same area may experience stronger seismic shaking than others. The reason behind such difference is that the effect of rock and soil conditions at sites, and variations in the propagation of seismic waves from the earthquake due to complexities in the structure of the Earth's crust. During an earthquake, the propagation of seismic waves through the soil column alters the amplitude, frequency, and duration of ground motion by the time it reaches the surface. The in uence of the underlying soil on the local ampli cation of earthquake shaking is called the site effect. The analysis to determine the site effect is called assessment of ground response or soil ampli cation (USGS, 2016b).
There are several methods for evaluating the effect of local soil conditions on ground response during earthquakes. Most of these methods are based on the assumption that the main responses in a soil deposit are caused by the upward propagation of shear waves from the underlying rock formation. Some of the commonly used methods and tools for analysis of ground response or soil ampli cation are SHAKE (Idriss & Sun, 1992;Schnabel, Lysmer, & Seed, 1972;USGS, 2011;Wald, Lin, Porter, & Turner, 2008;Wald, Wald, Worden, & Goltz, 2003;Wald, Worden, Quitoriano, & Pankow, 2005, 2006, DEEPSOIL (Hashash, Groholski, Phillips, Park, & Musgrove, 2011;Hashash & Park, 2001), PLAXIS (Brinkgreve, 2002;Brinkgreve et al., 2011), etc. These methodologies are similar. Thus, in this research DEEPSOIL software was used to analyze ground response or soil ampli cation of the study area integrating it with GIS.
To study soil ampli cation, rstly the borehole test data of the study area at different points and the ground shaking attributes computed in RADIUS considering the scenario earthquake were inputted in DEEPSOIL. After running the software, soil ampli cation factors for each of the points were generated. Then, the point data of soil ampli cation factors were collected and inputted in ArcGIS 10.3.1. The ground response or soil ampli cation map of the study area was prepared in ArcGIS 10.3.1 through the Kriging interpolation method to convert the two-dimensional point data into plane data. Kriging interpolation is one of the most suitable methods for geological and geotechnical predictions in space which is known as optimal interpolation method and is less arbitrary than others are (C. K. Chung, Kim, & Sun, 2014;D.-S. Kim, Chung, Sun, & Bang, 2002;Liu, Li, Jiang, Frattini, & Crosta, 2016;Sun, Chun, Ha, Chung, & Kim, 2008). In this way, a soil ampli cation map of the study area was prepared in this research.

Assessment of Soil Liquefaction Susceptibility
Soil liquefaction is a phenomenon where saturated sand and silt take on the characteristics of a liquid during the intense shaking of an earthquake, which occurs mainly in regions of the man-made land ll, especially ll-in areas that were once submerged (USGS, 2016a). Several factors in uence liquefaction such as the geologic history of the deposit, the depth of groundwater table, the grain size distribution, the density of soil, and ground slope. During an earthquake, in uenced by these factors, soil completely loses strength and stiffness because of excess pore water pressure developed due to monotonic or cyclic loads induced by the ground shaking. Thus, if saturated sand is subjected to ground vibrations, it tends to compact and decrease in volume. In such condition, if drainage is unable to occur, the tendency to decrease in volume increases pore water pressure, and subsequently if the pore water pressure builds up to the point at which it is equal to the overburden pressure, the effective stress becomes zero, the sand loses its strength completely, and then it develops a lique ed state (Marto & Tan, 2012). Because of such phenomena, buildings may sink substantially into the ground or tilt excessively, lightweight structures may oat upwards to the ground surface and foundations may displace laterally causing structural failures.
Liquefaction susceptibility is the physical characteristic of soil that determines whether it can liquefy. Soils that are susceptible to liquefaction typically have no to low plasticity, and low to moderate permeability (Sonmez, 2003). Recently, several methods for evaluating the liquefaction potential of sandy soil due to earthquake motions were developed, which can be classi ed into four categories (Sonmez, 2003). These categories are topographical and geological features analysis; the penetration test; laboratory cyclic shear testing of undisturbed sample; and in-situ blasting or laboratory shake table testing. Table 3 shows a comparison among the soil liquefaction susceptibility assessment methods.  (Cetin et al., 2004;Iwasaki et al., 1982;Kaljahi & Babazadeh, 2012;Harry Bolton Seed & Idriss, 1971H Bolton Seed, Wong, Idriss, & Tokimatsu, 1986;Sonmez, 2003;Stark & Olson, 1995) Laboratory cyclic shear testing of undisturbed sample Source: Adapted from (Sonmez, 2003) To analyze soil liquefaction susceptibility of the study area, SHAKE and DEEPSOIL were used in this research considering their simplicity and usefulness for mapping liquefaction susceptibility in large areas. The software uses topographical features and subsoil investigation data consisting of SPT-N value evaluation methods. For this purpose, rstly the borehole test data of the study area at different points and ground shaking attributes computed in RADIUS considering the scenario earthquake were used. After running the software, soil liquefaction susceptibility at each of the points was generated. Then, the point data were collected and inputted in ArcGIS 10.3.1. After that, the soil liquefaction susceptibility map of the study area was prepared in ArcGIS 10.3.1 through the Kriging interpolation method to convert the two-dimensional point data into plane data. In this way, the soil liquefaction susceptibility map of the study area was prepared in this research.

Mapping Seismic Hazard of the Study Area
In this research, the soil ampli cation map and soil liquefaction susceptibility map of the study area was developed based on the ground shaking attributes developed by RADIUS. Overlaying thee maps, the seismic hazard map was prepared for the study area in ArcGIS 10.3.1. Among different approaches, "Fuzzy Overlay" is a raster overlaying method, which follows fuzzy logic. It is applicable in such cases where it may become necessary to overlay raster data with different scales and units, and having inaccuracies in attribute and the geometry of spatial data. It speci cally addresses situations when the boundaries between classes are not clear (ESRI, 2016a;Kainz, 2007;Yanar & Akyürek, 2004). It is based on set theory; therefore, it de nes possibilities that how likely it is that the phenomenon is a member of a set (or class), not probabilities (ESRI, 2016a;Kainz, 2007;Mesgari, Pirmoradi, & Fallahi, 2008;Yanar & Akyürek, 2004).
In this research, liquefaction susceptibility and soil ampli cation are different phenomena and are represented in different units. Moreover, it is very di cult to classify these phenomena into distinct classes, e.g. high, moderate, low, etc. Therefore, the "Fuzzy Overlay" method was applied to prepare the seismic hazard map in this research. This method involves two steps: i) Fuzzi cation, and ii) Fuzzy overlay.
Fuzzi cation is necessary to reclassify or transform the raster datasets to be overlaid on a 0 (no membership) to 1 (full membership) scale, identifying the possibility of belonging to a speci ed set (ESRI, 2016a;Kainz, 2007;Mesgari et al., 2008;Yanar & Akyürek, 2004). In ArcGIS 10.3.1, it can be done using the "Fuzzy Membership" tool. To aid this transformation process, there are several membership functions in the "Fuzzy Membership" tool, i.e. fuzzy Gaussian, fuzzy large, fuzzy mean and standard deviation based large, fuzzy linear, fuzzy near, fuzzy small, and fuzzy mean and standard deviation based small (ESRI, 2016b). Among them "Fuzzy Large" is used when the larger input values are more likely to be a member of the set. The de ned midpoint identi es the crossover point (assigned membership of 0.5) with values greater than the midpoint having a higher possibility of being a member of the set and values below the midpoint having a decreasing membership. This assumption is consistent with the characteristics of the soil ampli cation and soil liquefaction susceptibility. So, the "Fuzzy Large" function of the "Fuzzy Membership" tool was utilized to reclassify the raster maps in ArcGIS 10.3.1.
After fuzzi cation of raster data, fuzzy overlaying was done. For this purpose, several techniques are available; i.e. fuzzy and, fuzzy or, fuzzy product, fuzzy sum, and fuzzy gamma. Each approach provides a different aspect for each cell's membership to the multiple input criteria (ESRI, 2016c). Among them, "Fuzzy Or" returns the maximum value of the sets the cell location belongs to. This technique is useful when it is necessary to identify the highest membership values for any of the input criteria, which is applicable in seismic hazard assessment. If an area is susceptible to either soil ampli cation or soil liquefaction, it is considered vulnerable. Therefore, the "Fuzzy Or" function of the "Fuzzy Overlay" tool was applied to overlay the reclassi ed raster maps of soil ampli cation and soil liquefaction susceptibility. Thus, the seismic hazard map of the study area was prepared.

Preparation of Individual Risk Theme Based Maps
Based on conditions described in Table 1, corresponding risk theme-based risk sensitivity maps of the land use plan of the study area were prepared in ArcGIS 10.3.1. For macro form risk, the prepared seismic hazard map of the study area was reclassi ed into ve classes using the "Raster Reclassi cation" tool considering the equal interval method as per Table 1. For distance-based risk themes and corresponding attributes, like risks in urban texture (primary road), risks in hazardous uses, special risk areas (waterbody), open space scarcity risk, and risks in critical facilities (educational facility and public facility, and health facility), "Create Buffer" tool was utilized considering the conditions described in Table 1. For this purpose, corresponding shape les of land use in the study area, like primary road, hazardous use, waterbody, open space, educational and public facility, and health facility, were utilized. For special risk areas (geomorphic suitability), the soil suitability data of GSB was reclassi ed into ve classes using the "Raster Reclassi cation" tool through de ning the classes as per GSB classi cation shown in Table 1 (GSB, 2016).

Mapping Earthquake Risk Sensitivity of Land Use Plan
After preparation of the risk theme-based risk sensitivity maps, the comparative weights of the risk themes were determined through Analytical Hierarchical Process (AHP) for overlaying these maps and produce the ultimate map showing earthquake risk sensitivity of land use plan (Saaty, 1977). For this purpose, key informant interviews were carried out. Firstly, a list of experts was developed considering the relevance of their expertise with this research that was further veri ed by each interviewee. Then the experts were interviewed to carry out a pairwise comparison table for the attributes. The a liation of the seven experts While interviewing the experts, they were rst explained about the con dentiality of the interview taking their permission duly. After that, the experts were informed about the purpose of this research and the procedure of the interview. Followed by that, the interviews were carried out. Here, responses with Consistency Index (CI) less or equal 10% was considered as a valid response. Based on the comparative weights of different risk themes, the corresponding theme-based risk sensitivity maps were overlaid using the "Weighted Overlay" tool in ArcGIS 10.3.1. Finally, this overlaid map shows the spatial multi-criteria earthquake risk sensitivity zoning of the land use plan of the study area.

Macro-Form Risk
Macro form risk in the study area was assessed considering seismic hazard assessment. According to (CDMP, 2009), earthquake scenarios were selected based on a seismic hazard assessment study in Dhaka city carried out by OYO International Corporation (OIC). Table 4 shows the earthquake scenarios for Dhaka city. Among them, the rst case was selected as the scenario earthquake for seismic hazard assessment in the study area considering that the scenario would produce the highest-level ground motion in the city. Figure 4 shows the Peak Ground Acceleration (PGA) in the study area for the selected scenario earthquake. From the map, it is visible that the northeastern portion of the study area is more prone to earthquake shaking.  (2009) Based on the ground shaking map (Figure 4), soil ampli cation factor and soil liquefaction susceptibility in the study area were analyzed which are shown in Figure 5 and Figure 6 respectively. To analyze the earthquake hazard in the study area, the maps were converted into the same base through fuzzy membership. Figure 7 and Figure 8 show the fuzzy membership of the ampli cation factor and soil liquefaction susceptibility in the study area accordingly. Figure 9 shows the seismic hazard map of the study area that was developed overlaying the fuzzy membership maps of ampli cation factor and soil liquefaction susceptibility. Here low earthquake hazard represents low-risk sensitivity and high earthquake hazard represents high-risk sensitivity from a macro form risk perspective. From the Figures, it can be observed that the western portion of the study area is most prone to seismic hazard and thereby most sensitive to earthquake risk from a macro form risk perspective.

Risks in Urban Texture
Risk in urban texture was assessed based on the road network in the study area as means of escape and access to rescue and relief. According to the land use plan of the study area, the roads are arranged in a grid pattern. The road width varies between 20 ft. to 210 ft. From the analysis of the road network and their width, it can be said that the proposed road network ensures interconnectedness within the study area, which is suitable for emergency movement, rescue and relief activities after an earthquake (Dill, 2004). Along with roads for vehicular movement, there are interconnected lakes and walkways along them, which can also be utilized as an alternative to road transport.
The primary road network also re ects the connectedness of the study area with other parts of the city. It is crucial for getting support from surrounding areas for the response, rescue, and relief. Thus, areas nearer to the primary roads are less risk-sensitive and vice versa from risk in urban texture. Figure 10 shows the risk sensitivity of the study area to distance from primary roads representing risk in urban texture. From the Figure, it can be observed that a major portion of the study area is covered within 500 meters from the primary roads signifying lower risk sensitivity.

Risks in Hazardous Uses
Risk in hazardous uses in the study area was assessed based on proximity from hazardous uses. In the study area, hazardous land use comprises 2.25% of the total area. There are together nine proposed gas stations and petrol pumps, collectively six power and substations proposed, MRT depot, provision for waste management and recycling area in 0.13 square km land, and a water treatment plant with an area of 0.03 square km. The areas nearer to hazardous uses are highly sensitive to risks in hazardous uses due to the higher possibility to be affected by secondary disasters. Figure 11 shows the risk sensitivity of the study area to distance from hazardous uses.

Geomorphic Suitability
The geomorphology of Dhaka city including geomorphologic composition and the suitability for construction analyzed based on a geomorphologic composition by GSB (GSB, 2016). From the geomorphologic composition of the study area, it can be observed that a major portion of the study area (about 67.1%) consists of the oodplain, marshy land and depression, and Madhupur terrace. Therefore, about 71.21% of the study area comprise of very weak foundation condition for which specialized foundation design is required, 17.58% is in weak foundation condition for which pile foundation is required, and 11.21% is suitable for all kind of infrastructure. Thus, grounded on the suitability for construction analyzed based on a geomorphologic composition by GSB, the weaker the soil condition the higher is the risk sensitivity due to the higher possibility to be affected by ampli cation and liquefaction. Figure 12 shows the risk sensitivity of the study area to geomorphic suitability.

Distance from Waterbody
From the analysis of water bodies in the land use plan of the study area, it can be observed that there are interconnected lakes proposed to be preserved by RAJUK comprising about 7.67% of the total study area. Here, the nearer the area is from the water body, the higher is the risk sensitivity due to the higher possibility to be affected by liquefaction. Figure 13 shows the risk sensitivity of the study area to distance from the waterbody.

Open Space Scarcity Risk
Open spaces are the priority to be used as evacuation space during and after an earthquake. According to the proposed plan of the study are, about 7.69% area is designated as open spaces. These include parks, play elds, graveyards, green belt, and other open space. The play elds are evenly distributed in all the neighborhoods adjacent to the educational facilities. The parks are mostly located along the lakes. The green belt is located along the southeastern sides of Sector 18 to separate the waste management plant, water treatment plant, and the neighborhoods from the water retention area located at the southern portion of the area. The open spaces are well connected by road networks, and the parks along the lake are connected by walkways. Such connectedness of the open areas makes them suitable for emergency response after an earthquake. The far the area is from open spaces, the higher is the risk sensitivity due to greater time to reach out for evacuation. Figure 14 shows the risk sensitivity of the study area to distance from open space. From the Figure, it can be observed that the study area is covered within 800 meters from the primary roads signifying lower risk sensitivity.

Potential Temporary Shelters
Educational and religious facilities in the study area are suitable to be used for temporary shelter. The far the area is from the potential temporary shelters, the higher is the risk sensitivity due to the greater time to reach out for shelter after an earthquake. Figure 15 shows the risk sensitivity of the study area to distance from potential temporary shelters.

Health Facilities
Health facilities are crucial to provide immediate treatment to injured people after an earthquake. The health facilities proposed in the study area include one hospital block and a club with a 0.03 square km area. Nevertheless, the facility is located at the northwestern corner of the study area, which may not be accessible by all the residents of the study area after an earthquake. The far the area is from the health facilities, the higher is the risk sensitivity due to the greater time to reach out for treatment after an earthquake. Figure 16 shows the risk sensitivity of the study area to distance from health facilities. Table 5 shows the ndings from AHP including comparative weight and rank of the risk attributes. From the analysis, it was observed that the highest preference was given to distance from geomorphic suitability, distance from hazardous use, and distance from the health facility. The experts think that the damage due to the earthquake will be greater if the soil condition is not considered while building construction. Moreover, hazardous uses will increase the risk of secondary hazards ( re, explosion, environmental pollution, etc.). Again, few health facilities will cause the death of injured people due to a lack of treatment facilities. Distance from health facilities 13.40% 3 Figure 17 shows the earthquake risk-sensitivity zoning map of the study area depicting the spatial earthquake risk sensitivity of the land-use plan. It was prepared by overlaying the risk attribute map using the weights determined in Table 5. From the map, it can be observed that sector 18 is more risk-sensitive though sector 16 is highly earthquake hazard-prone. The reasons behind such greater risk sensitivity of sector 18 are lack of connectivity with the rest of the city in the southern direction, location of hazardous uses especially the waste management area and water treatment plant, and inadequate health facilities. Thus, risk factors related to land use resulted in increased risk sensitivity of sector 18.

Conclusion
In conclusion, it can be said that the assessment of risk sensitivity of land use plan is of utmost importance to pave the way for developing effective and sustainable RSLUP strategies re ecting the risk sensitivity accordingly, which will further increase disaster resiliency of an area. This research brings out a methodology to assess spatial earthquake risk sensitivity of a land use plan and to prepare a multi-criteria risk-sensitivity zoning map. The study area considered here is a local area at the development phase. The local-level analysis brings out the detailed and actual scenario more accurately, enabling the development of RSLUP strategies with more relevance and effectiveness. Again, the development status would allow some scope to revise the land use plan through RSLUP with some freedom.
From the spatial earthquake risk sensitivity assessment of the land use plan of the study area, positive features can be observed in the case of risks in urban texture and open space scarcity risk. The proposed grid-patterned road network and width ensures interconnectedness within the study area for emergency movement within the area facilitating means of escape and access to rescue and relief after an earthquake. The open spaces are evenly located and well connected, which are effective and su cient to serve the area at the time of emergency after an earthquake. However, major earthquake risks in the study area lie in macro form risk, risks in hazardous uses, special risk areas, and risks in critical facilities. The study area is highly earthquake hazard-prone. The geomorphic condition increases the risk sensitivity of land use in the study area. There is only one large health facility located at the northwestern corner of the study area.
From the multi-criteria risk-sensitivity zoning map of the study area, it can be observed that sector 18 is more risk-sensitive though sector 16 is highly earthquake hazard-prone. Thus, risk factors related to land use resulted in increased risk sensitivity of sector 18. Overall, it can be said that earthquake risk sensitivity was not considered or integrated into the proposed land use plan of the study area.
The ndings from this research shows that assessment of risk sensitivity provides a guideline for the policymakers to understand "where" and "why" RSLUP interventions are necessary in a land use plan. These will further guide the "what" and "how" the RSLUP measures can be developed considering development status of an area, because drastic changes may not be possible to be accommodated. For example, the ndings in this research indicate that development in the study area should consider soil condition, few more health facilities should be allocated ensuring even distribution, land use plan and development in Sector 18 should be revisited considering the earthquake risk sensitivity, etc. Further research should be carried out on developing methodology for RSLUP based on the assessed risk sensitivity.
This method can be mainstreamed and institutionalized in planning policies and practices, which will ensure development of a resilient built environment.
Thus, research can be replicated in other areas of Bangladesh as well as in other countries with necessary modi cation considering local perspectives and hazard scenarios. In this research, AHP has been utilized for weight determination for producing the earthquake risk sensitivity map. However, there are multiple approaches to establishing weights, which may or may not need AHP. Therefore, this step can be completed in different ways suitable in in different contexts. In the future, research should be carried out to assess the risk sensitivity of land use considering multiple hazards simultaneously.

Declarations
Funding: This work was funded and supported by the Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh.
Interests: The authors have no relevant nancial or non-nancial interests to disclose. Soil ampli cation factor in the study area Soil liquefaction susceptibility in the study area Fuzzy membership of soil ampli cation factor in the study area  Seismic hazard map of the study area Figure 10 Risk sensitivity of the study area to distance from primary roads Figure 11 Risk sensitivity of the study area to distance from hazardous uses Figure 12 Risk sensitivity of the study area to geomorphic suitability Figure 13 Risk sensitivity of the study area to distance from waterbody Figure 14 Risk sensitivity of the study area to distance from open space Figure 15 Risk sensitivity of the study area to distance from the potential temporary shelter Figure 16 Risk sensitivity of the study area to distance from health facility Figure 17 Earthquake risk sensitivity zoning map of the study area