Designing Smart City using Genetic Algorithm in GIS


It is estimated that by year 2050, the number of people living in Indian cities will touch 843 millions. To accommodate this massive urbanization, India needs to find smarter ways to manage complexities, reduce expenses, increase efficiency and improve the quality of life. Rapid and unprecedented population growth has contributed to common, pressing issues for Indian cities. Many of these are inherently linked to transportation, including reducing urban sprawl, ensuring safe access to city services, and addressing the real estate industries’ roles in determining cities’ designs. There are some other problems like traffic congestion, power loads, drainage blockage, noise pollution, water pollution, air pollution, etc. due to a great number of facilities being developed in a specific region. This clustering of facilities is disturbing the normal life of citizens. So it is required to plan each city or a region in a way such that the different types of facilities (i.e. residential, commercial, recreational, transport, industrial, communication, etc.) must be situated with respect to their need having certain distance constraints specified by a planner among them.

Genetic Algorithm:

Genetic algorithms are used now a days for solving problems with optimal outputs. Its integration with GIS can help to solve problems regarding distribution of various kinds of facilities over a region with respect to certain fitness functions. GIS is the smart solution for developing a city. It may take all required consideration i.e. area, distance slope, etc. which is primary data used to develop any area or city. GIS can also provide both 2D and 3D views of features which can help in decision making and better visualization for future development. Read more about Genetic Algorithm tools for solving GIS based problems.


    Well distribution of facilities with respect to various measures like ensuring minimum distance between them.
    Roads can be designed according to the another set of fitness functions or else can be designed between the empty space left after optimal allocation of the facilities in a region.


GIS can be enabled to generate optimal solutions for planning any region defining the appropriate location for various kinds of facilities. Integration of Genetic algorithm will help to generate number of possible optimal solutions. This way a planner has the choice to select any of the plans generated.

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Aruna Rani
Aruna Rani
2 years 9 months ago

Good efforts. The algorithm can also be developed on the basis of available square ft area for smart city.

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