Use of GIS and Multi-criteria Analysis for Land Use Planning in Developing Countries: A case study of Land Use Suitability Analysis for Urban Development of Lalitpur District Nepal

doi:10.21203/rs.3.rs-3239924/v1

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Use of GIS and Multi-criteria Analysis for Land Use Planning in Developing Countries: A case study of Land Use Suitability Analysis for Urban Development of Lalitpur District Nepal

https://doi.org/10.21203/rs.3.rs-3239924/v1

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Nepal is facing rapid urbanization leading to unplanned land development/use. This research paper focuses on conducting a comprehensive land use suitability analysis for urban development in Lalitpur District, Nepal. By employing Geographic Information System (GIS) and Multi-criteria Analysis (MCA) techniques, this study aims to provide valuable insights into identifying suitable area for urban development and facilitating valuable insights into identifying suitable area for urban expansion and opening doors for informed decision making by planner and policy makers. Factors such as road network, existing infrastructure which include land cover, population density, flood sensitivity, slope, digital elevation model (DEM), (brick factory, land fill site), the study identified suitable areas for urban development in Lalitpur District. We divided the study area into four suitability classes: highly suitable, moderately suitable, marginally suitable, and not suitable. From our case study we found out that the highly suitable regions are typically flat, have low population densities, areas close to infrastructure, far away from landfill site, flood region and brick factory. Moderate suitable areas are typically hilly, have higher population densities, and areas farther from infrastructure. Marginal suitable areas are in floodplains, have steep slopes, and are near to industrial areas. Not suitable areas are too steep, highly prone to flood, too close to industrial areas, and have high population density. The findings of the analysis emphasize the importance of considering environmental factors in land use planning and urban development. It highlights the need to preserve green spaces, protect water bodies, and ensure the long-term well-being of residents. The results provide valuable insights for decision-makers and urban planners, enabling them to make informed decisions and allocate resources effectively.

Land use planning

Geographic Information System (GIS)

Multi-criteria Analysis (MCA)

Suitability analysis

Urban expansion

Environmental factors

Urban development is a complex process that involves a variety of factors, including population growth, economic development, and environmental sustainability. In recent years, the world has seen a rapid increase in urbanization, and this trend is expected to continue in the coming decades (Szpilko, 2020). In Nepal, urbanization has been rapidly progressing, particularly in regions surrounding Lalitpur. The unprecedented urban growth in Lalitpur District, has brought forth significant challenges such as haphazard development, inadequate infrastructure, and environmental degradation (Ishtiaque, Shrestha, & Chettri, 2017).This has led to a growing need for effective urban planning tools that can help to manage the growth of cities in a sustainable way. Infrastructure planning is essential for the efficient functioning of urban areas, and land use suitability analysis (LUSA) plays a vital role in this aspect. It assists in identifying suitable locations for transportation networks, utilities, and public facilities. By considering factors such as accessibility, existing infrastructure, and population distribution, LUSA helps optimize infrastructure planning ( Uy & Nakagoshi, 2008). This results in improved connectivity, reduced congestion, and enhanced livability in Lalitpur District.

It is crucial to adopt systematic approaches for land use planning and urban development. Geographic information systems (GIS) and multi criteria analysis (MCA) techniques offer effective tools to analyze and evaluate land suitability, enabling the identification of appropriate areas for urban expansion(Malczewski, 2004) ; (Prasai, 2022). Multi-criteria Analysis (MCA): MCA is a decision-making technique that allows for the integration of multiple criteria and their weights to assess and rank different alternatives. In this study, we used MCA to evaluate the suitability of land for various urban functions. Criteria such as accessibility, proximity to services, land slope, land cover, and environmental sensitivity was assigned weights based on expert opinions and literature review. The relative importance of each criterion was determined using the Analytic Hierarchy Process (AHP), and the Weighted Linear Combination (WLC) method was applied to aggregate and analyze the data.

Sustainable development is a key objective in land use planning, and LUSA contributes to achieving this goal in Lalitpur District. By considering environmental factors and integrating them into the analysis, LUSA helps identify environmentally sensitive areas and preserve natural resources and ecosystems. This promotes the conservation of green spaces, protection of water bodies, and maintenance of biodiversity (Green, et al., 2015). LUSA aids in creating a sustainable and resilient urban environment, ensuring long-term well-being. In addition, LUSA using GIS and MCA techniques holds immense importance for the urban development of Lalitpur District, Nepal. It facilitates optimal land allocation, supports sustainable development, aids in infrastructure planning, enhances disaster risk reduction measures, and enables informed decision-making. By employing LUSA, policymakers and planners can ensure planned and sustainable growth, improving the quality of life for residents while preserving the natural and cultural heritage of Lalitpur District. LUSA is a valuable tool in addressing the challenges posed by rapid urbanization and guiding urban development in a responsible manner.

The purpose of this study is to identify the most suitable areas in Lalitpur District for urban development. Land use suitability for urban development should be assessed based on factors that help to sustain human living standards and improve public health (Groot, 2006).We used GIS and MCA to evaluate the factors like land use, slope, distance to roads, and proximity to water bodies. The results of this study can be used to inform the development of a comprehensive urban plan for Lalitpur District.

Lalitpur district is one of the 77 districts of Nepal. It is located in the Bagmati Zone and is the headquarters of the Bagmati Province. The district is bordered by Makwanpur, Bhaktapur, Kathmandu, and Kavre. It has an area of 396.92 square kilometers and a population of 551667 as of the 2021 census. The district is home to a number of historical and cultural sites, including Patan Durbar Square, Krishna Temple, Central Zoo. Lalitpur is also known for its handicrafts, especially its metalwork and woodcarving. The district is home to a number of traditional festivals, including the Machhindranath chariot festival and the Bisket Jatra festival.

Lalitpur is a popular tourist destination, and the district is home to a number of hotels, restaurants, and other tourist amenities. The district is also well-connected by road and rail, making it easy to reach from other parts of Nepal.

The population growth at the Kathmandu Valley not only changed the shape of the city but also destroyed the open space. The change in area is shown in the table below:

Table 1

Comparison of different land cover class with time
Land Cover Class	Area (ha)
Land Cover Class	2000	2005	2010	2015
Urban	4583.63	62689.82	8146.23	9330.76
Suburban	1665.98	1708.15	1889.76	1896.80
Built-up rural	2112.39	2169.44	2313.95	2337.68
Open space (fringe)	4079.26	4266.33	4737.32	4898.34
Openspace (captured)	243.73	293.52	280.10	298.76
Open space (rural)	183503.56	181481.28	178812.19	177426.21
Open space (fringe, water)	0.95	0.95	0.95	0.95
Openspace (captured, water)	0	0	0	0
Open space (rural, water)	151.99	151.99	151.99	151.91

The table suggests that urban areas have experienced the most significant growth, indicating population migration towards urban centers. The slower growth in suburban and built-up rural areas may suggest that the available land within the urban area is nearing saturation, leading to future growth in these adjacent regions.

For the study area selection, this information provides insights into the spatial patterns of land cover change and population dynamics. It highlights the need to understand the preferences and demands of the population, as well as the need for proper planning and development strategies to accommodate future growth.

We obtained the data using google earth engine (Prasai, et al., 2021) and analyzed it in QGIS applying MCA technique

3.1 Data Collection:

Table 2

Datasets Collection and their sources used in the study
Datasets	Data source link	Data Source
RoadNetwork, Landfill, Infrastructure, CBD, Brick factory	https://download.geofabrik.de/index.html	Open street data
Land Cover	https://developers.google.com/earth-engine/datasets/tags/landcover	Google earth engine
Population Density	https://data.humdata.org/dataset/nepal-high- resolution-population-density-maps- demographic-estimates	CBS
DEM	http://earthdata.nasa.gov/earth-observation- data	Earth data
Sentinel 2 Imagery	https://sentinel.esa.int/web/sentinel/sentinel-data-access	European Space Agency

3.2 Data Preparation:

Flood sensitive area

We used the source data for all parameters except flood sensitivity. To create a flood severity map, we downloaded Sentinel-2 data from August 16, 2018, from the Sentinel Hub. This data captured the flood that occurred in Lalitpur on August 15, 2018. We then calculated the NDVI using the NIR and Red bands. We considered NDVI values less than 0 as water, indicating the flooded areas. We also properly monitored the unflooded areas. Finally, we used the output for the study.

Slope calculation:

The initial step is to work with a Digital Elevation Model (DEM), which provides information about the elevation values of the terrain across the study area. This raw DEM may contain sinks or depressions, which are areas where water would accumulate or flow inward, creating anomalies in the elevation data. Fill Sinks: Filling sinks in the DEM is a preprocessing step that aims to remove these depressions and ensure a continuous and consistent surface. By filling sinks, you are eliminating data anomalies and creating a more accurate representation of the terrain. This is crucial for subsequent terrain analysis. Slope Calculation: Once the sinks are filled, the resulting filled DEM provides a more reliable foundation for slope calculations. Slope calculations involve determining the steepness of the terrain at each location, which requires measuring the rate of elevation change in the steepest direction. By having a continuous and accurate representation of the terrain (after filling sinks), the slope calculation becomes more meaningful and freer from division by zero errors that could arise in the presence of sinks or flat areas.

Proximity CBD data:

The boundary of the CBD was digitized and converted into a point layer representing the CBD location. The buffer analysis tool in QGIS was employed to create buffer zones around the CBD point layer. Distances of 500 meters, 1000 meters, 1500 meters, 2000 meters, and more were chosen to represent different proximity ranges from the CBD. The buffer zones were generated by setting the respective distance values while keeping the buffer segment options appropriate for the study area.

Proximity to Landfill:

The boundary of the land fill was digitized and converted into a point layer representing the landfill location. The buffer analysis tool in QGIS was employed to create buffer zones around the landfill point layer. Distances of 500 meters, 1000 meters, 1500 meters, 2000 meters, and more were chosen to represent different proximity ranges from the landfill site. The buffer zones were generated by setting the respective distance values while keeping the buffer segment options appropriate for the study area.

Proximity to Infrastructure:

The point data of the infrastructures (education and health institution) was digitized representing the infrastructure location. The buffer analysis tool in QGIS was employed to create buffer zones around the infrastructure point layer. Distances of 500 meters, 1000 meters, 1500 meters, 2000 meters, and more were chosen to represent different proximity ranges from the infrastructure. The buffer zones were generated by setting the respective distance values while keeping the infrastructure segment options appropriate for the study area.

Proximity to Road:

The primary, secondary, and tertiary road data was digitized representing the road network. The buffer analysis tool in QGIS was employed to create buffer zones around the road network. Distances of 500 meters, 1000 meters, 1500 meters, 2000 meters, and more were chosen to represent different proximity ranges from the road network. The buffer zones were generated by setting the respective distance values while keeping the road network segment options appropriate for the study area.

Proximity to Brick factory:

The point data of the brick factory was digitized representing the brick factory location. The buffer analysis tool in QGIS was employed to create buffer zones around the brick factory point layer. Distances of 500 meters, 1000 meters, 1500 meters, 2000 meters, and more were chosen to represent different proximity ranges from the brick factory. The buffer zones were generated by setting the respective distance values while keeping the brick factory segment options appropriate for the study area.

3.3 Reclassification Based on Normalized Weight:

Table 3

Rank of each criterion
Rank Suitability grade	Very high opportunity 5	High opportunity 4	Moderate opportunity 3	Low opportunity 2	Very low opportunity 1
Land Cover (Luan, Renzhi, & Sicheng, 2021)	Bare Ground	Grass, Shrub	Built-up area	Trees, Crops	Water
Slope (Luan, Renzhi, & Sicheng, 2021)	0–17	17–33	33–48	48–67	67–89
Terrain Elevation (Luan, Renzhi, & Sicheng, 2021)	0-446	446–1016	1016–1589	1589–2000	2000–2375
Population Density	Very low density	Low density	Moderate density	High density	Very high density
Proximity to Infrastructure	< 500	500–1000	1000–1500	1500–2000	> 2000
Proximity to Road (Luan, Renzhi, & Sicheng, 2021)	< 500	500–1000	1000–1500	1500–2000	> 2000
Proximity to Brick Factory	> 2000	1500–2000	1000–1500	500–1000	< 500
Proximity to Landfill	> 2000	1500–2000	1000–1500	500–1000	< 500
Proximity to Sensitive Zone (Flood)	Not sensitive area	Mildly sensitive area	Moderately Sensitive area	Highly sensitive area	Extremely sensitive area
Proximity to CBD (Luan, Renzhi, & Sicheng, 2021)	< 500	500–1000	1000–1500	1500–2000	> 2000

The most suitable areas for urban development are those with bare ground, grass, or shrub cover, a slope of 0–17 degrees, a terrain elevation of 0-446 meters, low population density, proximity to infrastructure, proximity to the CBD, and distance from brick factories and landfills because these areas are relatively flat, have low population densities, and are convenient for residents and businesses (Howley, Scott, & Redmond, 2009). They are also less polluted and have a better quality of life.

The least suitable areas for urban development are those with built-up areas, trees, or crops, a slope of 17–89 degrees, a terrain elevation of 446–2375 meters, high population density, distance from infrastructure, proximity to brick factories and landfills, and location in flood sensitivity zones. These areas are already developed, may be difficult to develop, and may have traffic congestion, noise pollution, pollution, and flooding (Luan, Renzhi, & Sicheng, 2021).

The results of the proximity analysis can be used to inform decisions about where to locate new urban development projects. By considering the factors that were found to be most important, planners can help to ensure that new development is sustainable and contributes to the overall well-being of the community.

Here are some additional details about the factors that were considered in the proximity analysis:

Land cover: The land cover provides aggregate information on land. For example, a forest is a landcover but the type of the forest is land use. In this study as built-up area, water bodies, agriculture and forest land are given less value as compare to the barre land to protect the agriculture, forest area where as built-up area already contain settlement.
Slope: Slope is the angle of the land surface. Areas with a slope of 0–17 degrees are considered to be flat. Areas with a slope of 17–89 degrees are considered to be hilly. Areas with a slope of 89 degrees or more are considered to be mountainous.
Terrain elevation: Terrain elevation is the height of the land surface above sea level. Areas with a terrain elevation of 0-446 meters are considered to be low-lying. Areas with a terrain elevation of 446–2375 meters are considered to be mountainous.
Population density: Population density is the number of people per square kilometer. Areas with low population densities are considered to be more desirable for urban development than areas with high population densities.
Proximity to infrastructure: Infrastructure includes roads, schools, hospitals, and other essential services. Areas that are close to infrastructure are more convenient for residents and businesses.
Proximity to brick factories and landfills: Brick factories and landfills can pollute the environment and have a negative impact on the quality of life. Areas that are close to brick factories and landfills are less desirable for urban development.
Proximity to flood sensitivity zones: Flood sensitivity zones are areas that are prone to flooding. Areas that are located in flood sensitivity zones are less desirable for urban development.

3.4 Weighted Overlay Based on Influence:

We used the following evaluation to compare the importance/weights of individual parameters used in our model

Table 4

Evaluation between different data sets
Data sets	Influence (100%)	Reason
Land Cover	16	High percentage because it is the most important criteria for determining the existing land condition.
Slope	7	People general consider the slope because it is the cause of flood, landslide
Terrain Elevation	5	People consider elevation as people want to live in a place which has flat land and easy to build houses, crops etc.
Population Density	8	In our study we want to develop urban areas in the place which has the least population density in the present situation.
Proximity to Infrastructure	12	People want to live closer to the opportunity.
Proximity to CBD	11	The distance from CBD pulls or pushes people towards it.
Proximity to Brick Factory	6	Brick factories create pollution in the environment which may cause air related diseases.
Proximity to Road	14	People want accessibility and mobility.
Proximity to Landfill	9	Landfill is considered for better health.
Flood severity Map	12	For a safe place.

Our analysis classified the final output into four suitability classes: high suitable, moderate suitable, marginal suitable, and not suitable. The no data class, which represents small pixels that were not represented in the map, was not considered for the analysis.

High suitability: The analysis identified areas with high suitability as those that have the most favorable conditions for urban development. These areas are typically flat, have low population densities, and are close to infrastructure, far away from landfill site, flood region and brick factory.
Moderate suitability: The analysis found that areas with moderate suitability have some favorable conditions for urban development, but also have some limitations. These areas may be hilly, have higher population densities, or be further from infrastructure.
Marginal suitability: The analysis determined that areas with marginal suitability have some unfavorable conditions for urban development, but also have some potential for development. These areas may be in floodplains, have steep slopes, or be near industrial areas.
Not suitable: The analysis identified areas with not suitable conditions as those that are not suitable for urban development. These areas may be too steep, too flood-prone, or too close to industrial areas, have high population density.

Table 5

Areas in Lalitpur district with their suitability
Name of District	Suitability Area (km²)
Name of District	Highly Suitable	Moderately Suitable	Marginally Suitable	Not Suitable
Lalitpur	0.0234	146.0344	214.7742	36.0680

Our study used a weighted overlay approach to assess the suitability of different areas for urban development. This approach involves assigning weights to different criteria, such as land cover, slope, elevation, flood sensitivity, brick industry, landfill sites, infrastructure, and public health. The weights are then used to create a suitability map, which shows the areas that are most suitable for urban development.

The study found that the central and western parts of our case study area which is Lalitpur district are highly suitable for urban development. These areas are characterized by flat terrain, low population densities, and good access to infrastructure. The northeastern and southeastern parts of the district are moderately suitable for urban development. These areas have some limitations, such as steep slopes or high population densities, but they still have potential for development. The northern and southern parts of the district are marginally suitable for urban development. These areas have more limitations, such as floodplains or proximity to brick factories, but they could be developed with careful planning. The central and eastern parts of the district are not suitable for urban development due to high landslide risk, proximity to landfills, or other factors.

The results of this study can be used to inform decisions about where to locate new urban development projects in Lalitpur district. By considering the suitability of different areas, planners can help to ensure that new development is sustainable and contributes to the overall well-being of the community. The following are some of the recommendations that can be made based on the results of this study:

Highly suitable areas should be used for high-density development, such as residential and commercial areas.
Moderately suitable areas should be used for medium-density development, such as mixed-use areas and industrial areas.
Marginally suitable areas should be used for low-density development, such as agricultural and recreational areas.
Not suitable areas should be protected from development and used for conservation purposes.

It is important to note that these are just general recommendations. The specific development plans for each area will need to be developed in consultation with local stakeholders. The government and policy makers should also focus on mitigating the risks associated with development in the not suitable areas. This could include measures such as:

Relocating people who live in flood-prone areas.
Requiring developers to take measures to reduce pollution from brick factories.
Providing financial incentives for people to plant trees and shrubs in areas that are prone to flooding.
Encouraging the use of green building practices.
Promoting public transportation and non-motorized transportation.
Reducing the use of non-renewable resources.
Protecting the environment.
Discourage people to leave in the high-risk area by increasing tax, defining service boundaries.

By taking these steps, the government and policy makers can help to ensure that Lalitpur district is developed in a sustainable and safe manner. The study has some limitations. First, it is based on a single study area and may not be generalizable to other areas. Second, the study did not consider all of the potential factors that could affect the suitability of an area for urban development. Third, the study did not consider the economic and social impacts of different development scenarios. Despite these limitations, the study provides a valuable starting point for planning for future urban development in Lalitpur district. The study's findings can be used to inform decisions about where to locate new development projects, how to mitigate the risks associated with development, and how to ensure that new development is sustainable and contributes to the overall well-being of the community. By taking these steps, Lalitpur district can become a more sustainable and livable city.

Our study used a combination of Geographic Information System (GIS) and Multi-criteria Analysis (MCA) techniques to assess the suitability of different areas for a variety of urban functions, including residential, commercial, industrial, and agricultural use. The study found that the central and western parts of the district are the most suitable for urban development. These areas are characterized by flat terrain, low population densities, and good access to infrastructure. The northeastern and southeastern parts of the district are also suitable for urban development, but to a lesser extent. The northern and southern parts of the district are not suitable for urban development due to a variety of factors, including steep slopes, high population densities, and proximity to natural hazards.

The findings of this study have important implications for urban development in Lalitpur district. This study provides valuable insights for decision-makers, facilitating informed choices in sustainable urban development endeavors. It also highlights the importance of integrating GIS and MCA techniques in land use suitability analysis. In addition, it provides a valuable starting point for planning for future urban development in Lalitpur district. The study's findings can be used to inform decisions about where to locate new development projects, how to mitigate the risks associated with development, and how to ensure that new development is sustainable and contributes to the overall well-being of the community.

Author Contributions

Conceptualization: Narayan Thapa, Pragya Pant; methodology, Narayan Thapa, Pragya Pant, Ritika Prasai; Data cleaning: Narayan Thapa, Pragya Pant, Ritika Prasai, Aayush Mahata, Sujan Dulal; Data analysis and visualization: Narayan Thapa, Pragya Pant, Ritika Prasai, Aayush Mahata, Sujan Dulal: Writing—original draft preparation: Narayan Thapa, Ritika Prasai, Pragya Pant; All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Declaration of Competing Interest

None

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Use of GIS and Multi-criteria Analysis for Land Use Planning in Developing Countries: A case study of Land Use Suitability Analysis for Urban Development of Lalitpur District Nepal

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Abstract

Figures

1 Introduction

2 Study Area

3 Methods

3.1 Data Collection:

3.2 Data Preparation:

3.3 Reclassification Based on Normalized Weight:

3.4 Weighted Overlay Based on Influence:

4 Output criteria

5 Results and Discussion

6 Conclusion

Declarations

References

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