Geographic Information Systems in Urban Planning and Management

A geographic information system (GIS) is a computer system that is used to store, manage, analyse and display geospatial data. GIS has been used by professionals in natural resource management, land use planning, natural hazards, transportation, health care, public services, market area analysis and urban planning since the 1970s. It has also become a crucial tool for normal operations for government organizations at all levels. GIS integration with the Internet, GPS, wireless technology and web services has recently found applications in location-based services, web mapping, in-vehicle navigation systems, collaborative web mapping and donated geographic information. You should be able to understand the following after reading this chapter:

The fundamental requirement of Geographic Information Systems (GIS) is the use of spatially aligned map layers. If they do not, obvious errors can occur. GIS users are accustomed to working with map features on a plane surface. The map elements correspond to spatial features on the surface of earth. Map features are located using a plane coordinate system represented by x, y-coordinates, while spatial features are located using geographic coordinate system expressed by longitudes and latitudes. A map projection acts as the bridge between the two coordinate systems. Map projection converts the curved surface of the earth into a plane, yielding a map projection suitable for use with a projected coordinate system. Georeferencing is the process of connecting the internal coordinate system of a digital map or aerial photograph to a system of actual geographic coordinates on the ground. After completion of this chapter, you would be able to understand the following topics:

This chapter is concerned with the understanding of the raster and the vector data models of GIS and their data structure. After reading this chapter, you will understand the following:

This chapter discourses on the understanding of the process of data input in the GIS. It deliberates upon all the phases involved in the process from data collection to data preparation. After reading this chapter, you will understand the following:

This chapter is primarily concerned with comprehending the process of geovisualization as a transitional and final stage in the execution of GIS projects. You will understand the following after reading this chapter:

In the preceding chapters, we have explained the GIS data models, data types, data entry and editing in the Geographic Information System. In addition, digital data can be exploited for mapping and extracting information through spatial analysis for effective management of geographic objects/features/phenomena and identifying the new associations, thereby enhancing our knowledge of the real world. After reading this chapter you will be able to appreciate the following:

In previous units, you learned about GIS concepts, spatial data, data models and data structures. In a typical GIS, both spatial and non-spatial data are used. Spatial data describes the location of features, whereas non-spatial data describes their characteristics. Attribute data is another name for non-spatial data. Geospatial data is a combination of both types of data. It means that both (spatial and non-spatial) data are required for a GIS to function properly. In this chapter, we will go over the concept of non-spatial data in depth. On the completion of this chapter students will be able to learn the following concepts about non-spatial data/attribute data:

The combined use of GIS and Remote Sensing has widespread tremendously over the last few decades. We have studied the concepts of GIS and Remote Sensing in our previous chapters. It’s very necessary to understand the practical importance of these geospatial technologies rather than having only theoretical knowledge. In this chapter, it’s attempted to present before you all the burning fields which are being controlled and monitored by these technologies. After finishing this chapter, we will be capable of understanding the following GIS and Remote Sensing applications.

Land use-land cover (LULC) changes are an important indicator for urban planning and management. Understanding the patterns of LULC change aids in the effective management of all available resources, particularly in regions where there is little or no reported data on the status of LULC. In this study, remotely sensed satellite imagery from Landsat 5 and Landsat 8 was obtained for two years, 2001 and 2020, respectively. Geographic Information Systems (GIS) were used to quantify past and present LULC changes in Mirzapur District of Uttar Pradesh. To achieve these goals, the maximum likelihood classifier (MCL) was used to generate LULC maps with six class categories (water body, built-up land, forest land, crop land, barren land and fallow land). The classified maps for 2001 and 2020 were used to perform a two-decade change analysis over the region. The change analysis revealed that over the last two decades, built-up areas increased by 23.55% between 2001 and 2020. Barren and fallow land decreased by 8.37 and 5.77%, respectively, during this time period. The area under waterbodies has also decreased by 1.82%. These findings provide invaluable baseline information with which the government and other concerned stakeholders, urban planners and decision-makers can better manage available resources and monitor environmental changes in order to ensure the sustainable living in the region.

Developing nations are facing a serious threat of urbanization. India is one out of them. Keeping this concept on mind we tried to quantify the urban area expansion by using advanced geospatial technologies in this case study. Bangalore city was selected as our target study area. Here we assessed the urban area expansion from 2001 to 2025 in future. For that purpose, data for the years 2001, 2011 and 2016 was downloaded from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI). ERDAS Imagine was used for land use-land cover (LULC) map classification for the years 2001, 2011 and 2016. LULC map was classified into five LULC classes, viz. urban, forest, vegetation, fellow land and water body. Cellular Automata-Markov (CA-Markov) Chain Modelling was used through IDRISI Selva software for the prediction of future land cover. Land Transition Probability matrix was also created using Markov model for the time period 2001–2025. 2016 LULC map was used for the validation of CA–Markov Model. Result revealed that urban area will be increased to 33% in 2001 to 55% in 2025. Nearly forest cover will become half from 30% in 2001 to 15% in 2025. Vegetation will also reduce from 33% in 2001 to 24% in 2025. This case study revealed the fact that rapid rate of the urban area expansion is being observed in the region. But this is happening at the cost of forest and vegetation loss. Result showed that these two land use classes will decrease substantially till 2025. Considering this result, it is the responsibility of the concerned authorities and government stakeholders to prepare a wholistic plan in such a way that proper urbanization could be managed at the least cost of green cover in the region.

Due to the tremendous pace of population growth, the population is now expanding rapidly even in the hilly areas. It is now critical for policymakers and other stakeholders to look for potential built-up places to address the looming challenge. The process of choosing a built-up location is a multi-attribute decision problem. In hilly terrain, several heterogeneous criteria such as slope, road proximity, land use, distance from developed land, landslide, lithology, drainage proximity, lineament and aspect all play a role in site selection. The use of quantitative methodologies like Multi-Criteria Decision-Making (MCDM) techniques in land suitability procedures has expanded in the recent decade, allowing for the processing of diverse examined data. MCDM technique that was employed in this study to include uncertainties in decision-makers’ opinions is Analytical Hierarchy Process, (AHP). Slope was given highest preference for the selection of built-up sites in hilly areas, while lowest preference was given to distance from landslide and distance to lineament and aspect by the model. Result of model validation by using ROC curve reveals that AHP is able to delineate suitability class with 92% of accuracy. Final suitability map was prepared classifying it into 5 classes. The results indicate that the AHP indicates 4.32% as very less suitable, 15.15% as less suitable, 21.64% as high suitable and 31.50% as very high suitable. In conclusion, considering the rapid pace of urbanization, the possible built-up sites selection will lead to domino effect to secure holistic hill area development and planning.

Urban green space, a component of green infrastructure, refers to the areas covered with green vegetation including municipal parks, public playgrounds, etc., that are aimed to serve as the sites of recreation and environment enhancers in the city and planning regions. In this study, an urban green space analysis of NCT Delhi has been undertaken to understand its availability and distribution. Using the Sentinel-2A imagery, the existing green spaces of NCT Delhi were identified using the image classification. Vegetation, low vegetation, parks and golf course areas of NCT Delhi were extracted as urban green spaces. Per capita green space of NCT Delhi as a whole and for each district separately was quantified and mapped. According to World Health Organisation (WHO), a minimum of 9.5 m² of green space is required for each person for healthy living in the urban environment. NCT Delhi has per capita green space of 15.93 m²/person, which is greater than the threshold. However, four districts of the study area, i.e. Central Delhi (5.23 m²/person), Northeast Delhi (1.74 m²/person), East Delhi (5.21 m²/person) and West Delhi (5.84 m²/person) showcase acute shortage of green spaces per capita, necessitating the expansion of urban green spaces. Potential site selection for the expansion of the urban green space was carried out by buffering existing green spaces, water bodies and roads as central elements. The buffered areas were assigned weights and combined to generate the suitability map for the development of urban green spaces. The suitability map yields that NCT Delhi has a 47.92 km² area that is highly suitable for the expansion of green spaces and another 320.48 km² area that is moderately suitable for green space expansion.

Identification of suitable alternate landfill site is the Multi-Criteria Decision-Making (MCDM) problem. Several MCDM techniques are available to deal with such problem and used for decision-making. One such popular MCDM technique is Fuzzy TOPSIS which is applied in this case study for the purpose of alternate landfill site selection due to its ability to perform rational and appropriate decision-making in such types of problems. Varanasi is selected at the target region for assessing the best alternate landfill sites because Varanasi is facing serious threat of waste management in the region. To select the best alternate landfill sites in Varanasi, distance from waste production centre, distance from roads, depth to groundwater, distance from water bodies, distance from settlement land, types of soils and slope were considered. Out of these all seven criteria, four criteria, viz. distance from waste production centre, distance from roads, distance from settlement land and slope, were selected as beneficial criteria while rest three criteria, viz. depth to groundwater, distance from water bodies and types of soils, were considered as Non-beneficial (cost) Criteria. Five alternate sites, i.e. Khalispur, Bikapur, Rohania, Nuaon and Tikari, were selected to choose the best alternate among them. Result revealed that Rohania is the best alternate landfill site having highest Closeness Coefficient (0.899) and Tikari is the worst landfill site having lowest Closeness Coefficient (0.117).

Urban flooding (often referred to as water logging) is defined as the submergence of normally dry city areas by a considerable volume of water caused by heavy precipitation or overflowing of water bodies. Flood susceptibility modelling, by combining the effects of natural and human factors, determines the sensitivity of the space to flood hazard. Urban flood modelling has gained attention recently and since the incidence of urban floods has increased rapidly, due attention needs to be given to the urban flood studies. In this case study, urban flood susceptibility modelling of Srinagar City, Jammu and Kashmir, India, using Fuzzy MLPNN, has been carried out in Geographic Information System (GIS) environment. Fuzzy MLPNN is a simple and straightforward approach that unifies the complexity of the phenomenon of urban flooding by integrating fuzzy mathematics and machine learning to build a predictive model for the analysis of urban flood susceptibility using geospatial data. Eight flood conditioning factors (elevation, slope, profile curvature, plan curvature, geology, distance from natural streams, MFI and LULC) were used as independent variables along with urban flood locations as the dependent variable. A precursory FSZ map of Srinagar City was created using the frequency ratio technique, and non-flooded locations were accordingly determined. The developed model reveals the susceptibility of each and every pixel (12.5 × 12.5 m area) in the study area. The FSI, illustrated by the FSZ Map of Srinagar, demonstrates considerable susceptibility of the city to urban flood hazard. The dominant influence of spatiality of precipitation and water bodies is indicated by the conclusion that highly susceptible regions of the city are those where MFI is high and proximity to natural drainage is low. The FSZ map was validated using Area under the ROC Curve (AUC) Analysis, which substantiates the efficiency of the Fuzzy MLPNN model. With 0.931 and 0.922 AUC values, the success rate and predictive performance of the FSZ map come out to be excellent, respectively.

Urban Heat Island (UHI) refers to the manifestation of relatively higher surface and air temperatures in the urban centres with respect to the immediate surroundings where land use/land cover is generally green in nature (forest or agriculture). It is a local climate system in which the temperatures are observed to follow a slope in the direction radially outwards from the city centre. In this study, UHI of the Srinagar City Region has been mapped along the time series from 1991 to 2020 and the scenario has been predicted also for 2030. Land Surface Temperature (LST), retrieved from the thermal bands of Landsat 5 images for 1991, 1999 and 2010, and from Landsat 8 images of 2020 using Mono-window (MW) Algorithm was used to understand the temperature trend and the evolution of UHI zones in the Srinagar City region. The results show that the mean surface temperature of the study area has shifted from 16.04 ℃ in 1991 to 26.21 ℃ in 2020. In the same time period, the area of UHI zone has grown at a rate of 2.85 km² per year from 1991 to 2020. It expanded from 13.57 km² (1.82% of total area) in 1991 to 96.18 km² (12.90% of total area) in 2020. According to Multi-layer Perceptron Neural Network (MLPNN), which was used to predict the mean surface temperature, Srinagar City Region will experience 27.21 ℃ in 2030. This study also predicted the potential scenario of UHI zones for 2030 using Cellular Automata-Markov Chain Integrated Model (CA-Markov). It forecasts that in 2030, with an expansion rate of 12.57 km² per year, UHI zone of Srinagar City Region will expand to 221.94 km² covering 29.77% of the total area, if the present scenarios continue.