Land-use planning for sustainable urban development in Africa: a spatial and multi-objective optimization approach
Abstract
Land-use planning, which requires finding a balance among different conflicting social, economic and environment factors, is a complex task needed everywhere, including Africa. One example is the city of Zanzibar in Tanzania, which is under special consideration for land-use revision. From one side, the city has high potentials for tourist industry and at the other side there are major challenges with the city structure and poor accessibilities. In order to prepare a proper land-use plan for the city, a variety of influencing conflicting factors needs to be considered and satisfied. This can be regarded as a common problem in many African cities, which are under development. This paper aims to address the problem by proposing and demonstrating the use of Geographical Information System (GIS) and multi-objective optimization for land-use planning, in Zanzibar as a case study. The measures which have been taken by Zanzibar government to address the development challenges through the Zanzibar Strategy for Growth and Reduction of Poverty (ZSGRP) were identified by studying related documents and interviewing experts. Based on these, two objective functions were developed for land-use planning. Optimum base land-use plans were developed and mapped by optimizing the objective functions using the NSGA-II algorithm. The results show that the proposed approach and outputs can considerably facilitate land-use planning in Zanzibar. Similar approaches are highly recommended for other cities in Africa which are under development.
Keyword : Multi-Objective Optimization, Land-Use Planning, Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Zanzibar, Africa
How to Cite
Lubida, A., Veysipanah, M., Pilesjo, P., & Mansourian, A. (2019). Land-use planning for sustainable urban development in Africa: a spatial and multi-objective optimization approach. Geodesy and Cartography, 45(1), 1-15. https://doi.org/10.3846/gac.2019.6691
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abraham, A., Jain, L., & Goldberg, R. (2005). Evolutionary multiobjective optimization: Theoretical advances and applications. London: Springer-Verlag. https://doi.org/10.1007/1-84628-137-7
Aguirre, A. H., Rionda, S. B., Coello Coello, C. A., Lizárraga, G. L., & Montes, E. M. (2004). Handling constraints using multiobjective optimization concepts. International Journal for Numerical Methods in Engineering, 59(15), 1989-2017. https://doi.org/10.1002/nme.947
Ahmadi, F., & Toghyani, S. (2011). The role of urban planning in achieving sustainable urban development. OIDA International Journal of Sustainable Development, 2(11), 23-26.
Ananda, J., & Herath, G. (2009). A critical review of multi-criteria decision making methods with special reference to forest management and planning. Ecological Economics, 68(10), 2535-2548. https://doi.org/10.1016/j.ecolecon.2009.05.010
Anderson, W. (2013). Leakages in the tourism systems: case of Zanzibar. Tourism Review, 68(1), 62-76. https://doi.org/10.1108/16605371311310084
Auma, S. L. A. (2012). Integrating community participation for urban redevelopment planning in Zanzibar town. Enschede, The Netherlands.
Awadh, G. O. (2007). Tourism and Heritage Conservation: 21st Century Challenge. Case: Zanzibar Stone Town. Paper presented at the Manifestation of African Perspectives Dialogues on Urbanism and Architecture, Faculty of Architecture – TU Delft, The Netherlands.
Azzan, R. M., & Ufuzo, S. S. (2005). The role of spatial data and infrastructure in an information society: Conflicts and implications for Zanzibar. Paper presented at the FIG Working Week 2005 and GSDI-8 From Pharaohs to Geoinformatics, Cairo, Egypt.
Babakan, A. S., & Alimohammadi, A. (2015). An agent-based simulation of residential location choice of tenants in Tehran, Iran. Transaction in GIS. https://doi.org/10.1111/tgis.12144
Balling, R. J., Brown, M. R., & Day, K. (1999). Multiobjective urban planning using genetic algorithm. Journal of Urban Planning and Development, 125(2), 86-99. https://doi.org/10.1061/(ASCE)0733-9488(1999)125:2(86)
Balsem, T. (2011). Report of the International Land Use Planning Advisor’s Mission.
Bissell, W. C. (2011). Urban sustainability at the limits: Development rhetorics and realities in Tanzania. Development, 54(3), 317-324. https://doi.org/10.1057/dev.2011.63
Cao, K., Batty, M., Huang, B., Liu, Y., Yu, L., & Chen, J. (2011). Spatial multi-objective land use optimization: extensions to the Non-Dominated Sorting Genetic Algorithm-II. International Journal of Geographical Information Science, 25(12), 1949-1969. https://doi.org/10.1080/13658816.2011.570269
Coello, C. A. C., Van Veldhuizen, D. A., & Lamont, G. B. (2002). Evolutionary algorithms for solving multi-objective problems (2 Ed.). New York: Springer. https://doi.org/10.1007/978-1-4757-5184-0
Colomi, A., Dorigo, M., & Maniezzo, V. (1991). Distributed optimization by Ant colonies. Paper presented at the Proceedings of ECAL91 – European Conference on Artificial Life, Paris, France.
Creswell, J. W., & Plano Clark, V. L. (2007). Designing and conducting mixed methods research (Vol. 2009). Thousand Oaks, CA: SAGE Publications.
Daniels, R., & Mulley, C. (2013). Explaining walking distance to public transport: the dominance of public transport supply. Journal of Transport and Land Use, 6(2), 5-20. https://doi.org/10.1007/978-3-540-70928-2_32
Deb, K. (2001). Multi-Objective Optimization using evolutionary algorithms. Chichester: Wiley.
Deb, K. (2014). Multi-objective Optimization. In E. K. Burke & G. Kendall (Eds.), Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques (2 ed.). London: Springer. https://doi.org/10.1007/978-1-4614-6940-7_15
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A Fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. https://doi.org/10.1109/4235.996017
Dodman, D., McGranahan, G., & Dalal-Clayton, B. (2013). Integrating the Environment in urban planning and management – key principles and approaches for cities in the 21st century. Nairobi: United Nations Environment Programme.
Dorigo, M. (1992). Optimization, learning and natural algorithms (PhD Thesis). Polytechnic University of Milan, Milan, Italy.
Fonseca, C. M., & Fleming, P. J. (1993, 28 May). Multiobjective genetic algorithms. Paper presented at the IEE colloquium on “Genetic Algorithms for Control Systems Engineering”, London.
Forson, E. K. (2011). Estimating socio-economic value of cycling using opportunity cost methodology (Master of Science). Twente, Enschede.
Fudenberg, D., & Tirole, J. (1983). Game theory. MIT Press.
Haji, H. A., Azzan, R. M., & Ufuzo, S. S. (2006). Evolution of spatial planning in Zanzibar and its Influence. Paper presented at the XXIII FIG Congress, Munich, German.
Hall, A. (2009). A Green lung for the stone town: The challenge of developing a green structure for the users of Zanzibar historical city core (A Master Thesis in Landscape Architecture). Swedish University of Agricultural Sciences, Alnarp.
Haque, A., & Asami, Y. (2011). Optimizing urban land-use allocation: case study of Dhanmondi Residential Area, Dhaka, Bangladesh. Environment and Planning B: Planning and Design, 38(3), 388-410. https://doi.org/10.1068/b35041
Haque, A., & Asami, Y. (2014). Optimizing urban land use allocation for planners and real estate developers. Computers, Environment and Urban Systems, 46, 57-69. https://doi.org/10.1016/j.compenvurbsys.2014.04.004
Hikmany, A. H. (2012). Land planning authorities and sustainable tourism in Zanzibar. Paper presented at the FIG Working Week 2012: Knowing to Manage the Territory, Protect the Environment, Evaluate the Cultural Heritage, Rome, Italy. https://doi.org/10.2139/ssrn.2687645
Horn, J., Nafpliotis, N., & Goldberg, D. E. (1994, 27-29 June). A niched Pareto genetic algorithm for multiobjective optimization. Paper presented at the Proceedings of the first IEEE conference on evolutionary computation. IEEE world congress on computational intelligence, Orlando, FL, USA. https://doi.org/10.1109/ICEC.1994.350037
Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. New York: Springer-Verlag. https://doi.org/10.1007/978-3-642-48318-9
Jebari, K., & Madiafi, M. (2013). Selection methods for genetic algorithms. International Journal of Emerging Sciences, 3(4), 333-344.
Karaboga, D. (2010). Artificial bee colony algorithm. Scholarpedia, 5(3), 6915. https://doi.org/10.4249/scholarpedia.6915
Kennedy, J. (1997). The particle swarm: social adaptation of knowledge. Paper presented at the Proceedings of IEEE International Conference on Evolutionary Computation. IEEE. https://doi.org/10.1109/ICEC.1997.592326
Kennedy, J., & Eberhart, R. (1995). Particle Swarm optimization. Paper presented at the Proceedings of IEEE International Conference on Neural Networks, Perth, WA. https://doi.org/10.1109/ICNN.1995.488968
Khatib, A., Mmochi, A. J., Mpatane, M., & Kombo, M. (2004). Country Report Tanzania. Zanzibar.
Köksalan, M., Wallenius, J., & Zionts, S. (2011). Multiple criteria decision making: From early history to the 21st century. World Scientific Publishing Company. https://doi.org/10.1142/8042
Lichfield, N., Kettle, P., & Whitbread, M. (1975). Evaluation in the planning process. Oxford: Pergamon Press. https://doi.org/10.1016/B978-0-08-017843-1.50008-5
Ligmann-Zielinska, A., Church, R. L., & Jankowski, P. (2008). Spatial optimization as a generative technique for sustainable multiobjective land use allocation. International Journal of Geographical Information Science, 22(6), 601-622. https://doi.org/10.1080/13658810701587495
Liu, X., Ou, J., Li, X., & Ai, B. (2013). Combining system dynamics and hybrid particle swarm optimization for land use allocation. Ecological Modelling, 257(2013), 11-24. https://doi.org/10.1016/j.ecolmodel.2013.02.027
Lupala, J. M. (2002). Urban types in rapidly urbanising cities: Analysis of formal and informal settlements in Dar es Salaam, Tanzania (PhD), KTH, Stockholm.
Mahmoud, I. I. (2013). Inclusion of small-scale farmers in the spice value chain in Zanzibar, Tanzania. (MA). International Institute of Social Studies (ISS), The Hague.
Makame, M. K., & Boon, E. K. (2008). Sustainable tourism and benefit-sharing in Zanzibar: The Case of Kiwengwa-Pongwe forest reserve. Journal of Human Ecology, 24(2), 93-109. https://doi.org/10.1080/09709274.2008.11906105
Mansourian, A., Taleai, M., & Fasihi, A. (2011). A Web-based spatial decision support system to enhance public participation in urban planning process. Journal of Spatial Science, 56(2), 269-287. https://doi.org/10.1080/14498596.2011.623347
Martin-Moreno, R., & Vega-Rodriguez, M. A. (2018). Multiobjective artificial bee colony algorithm applied to the bi-objective orienteering problem. Knowledge-Based Systems, 154, 93-101. https://doi.org/10.1016/j.knosys.2018.05.005
Masoomi, Z., Mesgari, M. S., & Hamrah, M. (2012). Allocation of urban land uses by multi-objective particle swarm optimization algorithm. International Journal of Geographical Information Science, 27(3), 542-566.
Masoumi, Z., Maleki, J., Mesgari, M. S., & Mansourian, A. (2017). Using an evolutionary algorithm in multiobjective geographic analysis for land use allocation and decision supporting. Geographical Analysis, 49(1), 58-83. https://doi.org/10.1111/gean.12111
MathWorks. (2015). MATLAB and Statistics Toolbox Release 2012b. Natick, Massachusetts, United States.
Mosadeghi, R., Warnken, J., Tomlinson, R., & Mirfenderesk, H. (2015). Comparison of Fuzzy-AHP and AHP in a spatial multi-criteria decision making model for urban land-use planning. Computers, Environment and Urban Systems, 49(2015), 54-65. https://doi.org/10.1016/j.compenvurbsys.2014.10.001
Murata, T., & Ishibuchi, H. (1995, 29 November–1 December, 1995). MOGA: multi-objective genetic algorithms. Paper presented at the Proceedings of the 1995 IEEE international conference on evolutionary computation, Perth, WA, Australia.
Myers, G. A. (2008). Peri-urban land reform, political-economic reform, and urban political ecology in Zanzibar. Urban Geography, 29(3), 264-288. https://doi.org/10.2747/0272-3638.29.3.264
Neema, M. N., & Ohgai, A. (2010). Multi-objective location modelling of urban parks and open spaces: Continuous optimization. Computers Environment and Urban Systems, 34(5), 359-376. https://doi.org/10.1016/j.compenvurbsys.2010.03.001
RGoZ. (2007). Zanzibar strategy for growth and reduction of poverty (ZSGRP). Zanzibar.
RGoZ. (2011). Revisited Zanzibar Development Vision 2020. Zanzibar.
Saadatseresht, M., Mansourian, A., & Taleai, M. (2009). Evacuation planning using multiobjective evolutionary optimization approach. European Journal of Operational Research, 198(2009), 305-314. https://doi.org/10.1016/j.ejor.2008.07.032
Saaty, T. L. (1980). The analytic hierarchy process, planning, piority setting, resource allocation. New York: McGraw Hill.
Salim, I. S., & Mwaipopo, L. J. (2016). What satisfies tourists in cultural heritage sites? Evidence of Zanzibar Stone Town. Journal of Research in Hospitality, Tourism and Culture, 3(1), 1-10.
Sharpley, R., & Ussi, M. (2014). tourism and governance in Small Island Developing States (SIDS): The Case of Zanzibar. International Journal of Tourism Research, 16, 87-96. https://doi.org/10.1002/jtr.1904
Shaygan, M., Alimohammadi, A., Mansourian, A., Govara, Z. S., & Kalami, S. M. (2014). Spatial Multi-Objective Optimization Approach for Land Use Allocation using NSGA-II. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(3), 906-916. https://doi.org/10.1109/JSTARS.2013.2280697
Shi, Y., & Eberhart, R. C. (1998). A modified particle swarm optimizer. Paper presented at the Proceedings of IEEE International Conference on Evolutionary Computation. Piscataway, NJ. https://doi.org/10.1109/ICEC.1998.699146
Shifa, M., Jianhua, H. E., Feng, L. I. U., & Yan, Y. U. (2011). Land-use spatial optimization based on PSO algorithm. GeoSpatial Information Science, 14(1), 54-61. https://doi.org/10.1007/s11806-011-0437-8
Sida. (2004). A future for the past: historic cities in development. Stockholm.
Silva, C. N. (2015). Urban planning in Sub-Saharan Africa: colonial and postcolonial planning cultures. New York: Routledge. https://doi.org/10.4324/9781315797311
Sjöstrand, J. (2014). Cultivating authenticity: Perceptions of Zanzibari culture and history within the heritage management of Stone Town (Master’s Thesis in Urban and Regional Planning), Stockholm University, Stockholm.
Srinivas, N., & Deb, K. (1994). Multi-objective optimization using non-dominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221-248. https://doi.org/10.1162/evco.1994.2.3.221
Stewart, T. J., Janssen, R., & Van Herwijnen, M. (2004). A genetic algorithm approach to multiobjective landuse planning. Computers & Operations Research, 31(2004), 2293-2313. https://doi.org/10.1016/S0305-0548(03)00188-6
Ullah, K. M., & Mansourian, A. (2015). Evaluation of land suitability for urban land-use planning: case study Dhaka city. Transaction in GIS. https://doi.org/10.1111/tgis.12137
UN-Habitat. (2010). Planning sustainable cities: UN-habitat practices and perspectives. Nairobi.
UNESCO. (2000). Stone Town of Zanzibar. UNESCO.
Wang, & Li, Y. (2015, 12-14 December). Multi-objective artificial bee colony algorithm. 2015 International Conference on Computational Intelligence and Communication Networks (CICN). Jabalpur, India: IEEE.
Wang, H., Shen, Q., Tang, B., & Skitmore, M. (2013). An integrated approach to supporting land-use decisions in site redevelopment for urban renewal in Hong Kong. Habitat International, 38, 70-80. https://doi.org/10.1016/j.habitatint.2012.09.006
Xu, G., Yang, Y., Liu, B., Xu, Y., & Wu, A. (2015). An efficient hybrid multi-objective particle swarm optimization with a multi-objective dichotomy line search. Journal of Computational and Applied Mathematics, 280(2015), 310-326. https://doi.org/10.1016/j.cam.2014.11.056
Yan, G., & Li, C. (2011). An effective refinement artificial bee colony optimization algorithm based on chaotic search and application for PID control tuning. Journal of Computational Information Systems, 7, 3309-3316.
Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto Approach. IEEE Transactions on Evolutionary Computation, 3(4), 257-271. https://doi.org/10.1109/4235.797969
Aguirre, A. H., Rionda, S. B., Coello Coello, C. A., Lizárraga, G. L., & Montes, E. M. (2004). Handling constraints using multiobjective optimization concepts. International Journal for Numerical Methods in Engineering, 59(15), 1989-2017. https://doi.org/10.1002/nme.947
Ahmadi, F., & Toghyani, S. (2011). The role of urban planning in achieving sustainable urban development. OIDA International Journal of Sustainable Development, 2(11), 23-26.
Ananda, J., & Herath, G. (2009). A critical review of multi-criteria decision making methods with special reference to forest management and planning. Ecological Economics, 68(10), 2535-2548. https://doi.org/10.1016/j.ecolecon.2009.05.010
Anderson, W. (2013). Leakages in the tourism systems: case of Zanzibar. Tourism Review, 68(1), 62-76. https://doi.org/10.1108/16605371311310084
Auma, S. L. A. (2012). Integrating community participation for urban redevelopment planning in Zanzibar town. Enschede, The Netherlands.
Awadh, G. O. (2007). Tourism and Heritage Conservation: 21st Century Challenge. Case: Zanzibar Stone Town. Paper presented at the Manifestation of African Perspectives Dialogues on Urbanism and Architecture, Faculty of Architecture – TU Delft, The Netherlands.
Azzan, R. M., & Ufuzo, S. S. (2005). The role of spatial data and infrastructure in an information society: Conflicts and implications for Zanzibar. Paper presented at the FIG Working Week 2005 and GSDI-8 From Pharaohs to Geoinformatics, Cairo, Egypt.
Babakan, A. S., & Alimohammadi, A. (2015). An agent-based simulation of residential location choice of tenants in Tehran, Iran. Transaction in GIS. https://doi.org/10.1111/tgis.12144
Balling, R. J., Brown, M. R., & Day, K. (1999). Multiobjective urban planning using genetic algorithm. Journal of Urban Planning and Development, 125(2), 86-99. https://doi.org/10.1061/(ASCE)0733-9488(1999)125:2(86)
Balsem, T. (2011). Report of the International Land Use Planning Advisor’s Mission.
Bissell, W. C. (2011). Urban sustainability at the limits: Development rhetorics and realities in Tanzania. Development, 54(3), 317-324. https://doi.org/10.1057/dev.2011.63
Cao, K., Batty, M., Huang, B., Liu, Y., Yu, L., & Chen, J. (2011). Spatial multi-objective land use optimization: extensions to the Non-Dominated Sorting Genetic Algorithm-II. International Journal of Geographical Information Science, 25(12), 1949-1969. https://doi.org/10.1080/13658816.2011.570269
Coello, C. A. C., Van Veldhuizen, D. A., & Lamont, G. B. (2002). Evolutionary algorithms for solving multi-objective problems (2 Ed.). New York: Springer. https://doi.org/10.1007/978-1-4757-5184-0
Colomi, A., Dorigo, M., & Maniezzo, V. (1991). Distributed optimization by Ant colonies. Paper presented at the Proceedings of ECAL91 – European Conference on Artificial Life, Paris, France.
Creswell, J. W., & Plano Clark, V. L. (2007). Designing and conducting mixed methods research (Vol. 2009). Thousand Oaks, CA: SAGE Publications.
Daniels, R., & Mulley, C. (2013). Explaining walking distance to public transport: the dominance of public transport supply. Journal of Transport and Land Use, 6(2), 5-20. https://doi.org/10.1007/978-3-540-70928-2_32
Deb, K. (2001). Multi-Objective Optimization using evolutionary algorithms. Chichester: Wiley.
Deb, K. (2014). Multi-objective Optimization. In E. K. Burke & G. Kendall (Eds.), Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques (2 ed.). London: Springer. https://doi.org/10.1007/978-1-4614-6940-7_15
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A Fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. https://doi.org/10.1109/4235.996017
Dodman, D., McGranahan, G., & Dalal-Clayton, B. (2013). Integrating the Environment in urban planning and management – key principles and approaches for cities in the 21st century. Nairobi: United Nations Environment Programme.
Dorigo, M. (1992). Optimization, learning and natural algorithms (PhD Thesis). Polytechnic University of Milan, Milan, Italy.
Fonseca, C. M., & Fleming, P. J. (1993, 28 May). Multiobjective genetic algorithms. Paper presented at the IEE colloquium on “Genetic Algorithms for Control Systems Engineering”, London.
Forson, E. K. (2011). Estimating socio-economic value of cycling using opportunity cost methodology (Master of Science). Twente, Enschede.
Fudenberg, D., & Tirole, J. (1983). Game theory. MIT Press.
Haji, H. A., Azzan, R. M., & Ufuzo, S. S. (2006). Evolution of spatial planning in Zanzibar and its Influence. Paper presented at the XXIII FIG Congress, Munich, German.
Hall, A. (2009). A Green lung for the stone town: The challenge of developing a green structure for the users of Zanzibar historical city core (A Master Thesis in Landscape Architecture). Swedish University of Agricultural Sciences, Alnarp.
Haque, A., & Asami, Y. (2011). Optimizing urban land-use allocation: case study of Dhanmondi Residential Area, Dhaka, Bangladesh. Environment and Planning B: Planning and Design, 38(3), 388-410. https://doi.org/10.1068/b35041
Haque, A., & Asami, Y. (2014). Optimizing urban land use allocation for planners and real estate developers. Computers, Environment and Urban Systems, 46, 57-69. https://doi.org/10.1016/j.compenvurbsys.2014.04.004
Hikmany, A. H. (2012). Land planning authorities and sustainable tourism in Zanzibar. Paper presented at the FIG Working Week 2012: Knowing to Manage the Territory, Protect the Environment, Evaluate the Cultural Heritage, Rome, Italy. https://doi.org/10.2139/ssrn.2687645
Horn, J., Nafpliotis, N., & Goldberg, D. E. (1994, 27-29 June). A niched Pareto genetic algorithm for multiobjective optimization. Paper presented at the Proceedings of the first IEEE conference on evolutionary computation. IEEE world congress on computational intelligence, Orlando, FL, USA. https://doi.org/10.1109/ICEC.1994.350037
Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. New York: Springer-Verlag. https://doi.org/10.1007/978-3-642-48318-9
Jebari, K., & Madiafi, M. (2013). Selection methods for genetic algorithms. International Journal of Emerging Sciences, 3(4), 333-344.
Karaboga, D. (2010). Artificial bee colony algorithm. Scholarpedia, 5(3), 6915. https://doi.org/10.4249/scholarpedia.6915
Kennedy, J. (1997). The particle swarm: social adaptation of knowledge. Paper presented at the Proceedings of IEEE International Conference on Evolutionary Computation. IEEE. https://doi.org/10.1109/ICEC.1997.592326
Kennedy, J., & Eberhart, R. (1995). Particle Swarm optimization. Paper presented at the Proceedings of IEEE International Conference on Neural Networks, Perth, WA. https://doi.org/10.1109/ICNN.1995.488968
Khatib, A., Mmochi, A. J., Mpatane, M., & Kombo, M. (2004). Country Report Tanzania. Zanzibar.
Köksalan, M., Wallenius, J., & Zionts, S. (2011). Multiple criteria decision making: From early history to the 21st century. World Scientific Publishing Company. https://doi.org/10.1142/8042
Lichfield, N., Kettle, P., & Whitbread, M. (1975). Evaluation in the planning process. Oxford: Pergamon Press. https://doi.org/10.1016/B978-0-08-017843-1.50008-5
Ligmann-Zielinska, A., Church, R. L., & Jankowski, P. (2008). Spatial optimization as a generative technique for sustainable multiobjective land use allocation. International Journal of Geographical Information Science, 22(6), 601-622. https://doi.org/10.1080/13658810701587495
Liu, X., Ou, J., Li, X., & Ai, B. (2013). Combining system dynamics and hybrid particle swarm optimization for land use allocation. Ecological Modelling, 257(2013), 11-24. https://doi.org/10.1016/j.ecolmodel.2013.02.027
Lupala, J. M. (2002). Urban types in rapidly urbanising cities: Analysis of formal and informal settlements in Dar es Salaam, Tanzania (PhD), KTH, Stockholm.
Mahmoud, I. I. (2013). Inclusion of small-scale farmers in the spice value chain in Zanzibar, Tanzania. (MA). International Institute of Social Studies (ISS), The Hague.
Makame, M. K., & Boon, E. K. (2008). Sustainable tourism and benefit-sharing in Zanzibar: The Case of Kiwengwa-Pongwe forest reserve. Journal of Human Ecology, 24(2), 93-109. https://doi.org/10.1080/09709274.2008.11906105
Mansourian, A., Taleai, M., & Fasihi, A. (2011). A Web-based spatial decision support system to enhance public participation in urban planning process. Journal of Spatial Science, 56(2), 269-287. https://doi.org/10.1080/14498596.2011.623347
Martin-Moreno, R., & Vega-Rodriguez, M. A. (2018). Multiobjective artificial bee colony algorithm applied to the bi-objective orienteering problem. Knowledge-Based Systems, 154, 93-101. https://doi.org/10.1016/j.knosys.2018.05.005
Masoomi, Z., Mesgari, M. S., & Hamrah, M. (2012). Allocation of urban land uses by multi-objective particle swarm optimization algorithm. International Journal of Geographical Information Science, 27(3), 542-566.
Masoumi, Z., Maleki, J., Mesgari, M. S., & Mansourian, A. (2017). Using an evolutionary algorithm in multiobjective geographic analysis for land use allocation and decision supporting. Geographical Analysis, 49(1), 58-83. https://doi.org/10.1111/gean.12111
MathWorks. (2015). MATLAB and Statistics Toolbox Release 2012b. Natick, Massachusetts, United States.
Mosadeghi, R., Warnken, J., Tomlinson, R., & Mirfenderesk, H. (2015). Comparison of Fuzzy-AHP and AHP in a spatial multi-criteria decision making model for urban land-use planning. Computers, Environment and Urban Systems, 49(2015), 54-65. https://doi.org/10.1016/j.compenvurbsys.2014.10.001
Murata, T., & Ishibuchi, H. (1995, 29 November–1 December, 1995). MOGA: multi-objective genetic algorithms. Paper presented at the Proceedings of the 1995 IEEE international conference on evolutionary computation, Perth, WA, Australia.
Myers, G. A. (2008). Peri-urban land reform, political-economic reform, and urban political ecology in Zanzibar. Urban Geography, 29(3), 264-288. https://doi.org/10.2747/0272-3638.29.3.264
Neema, M. N., & Ohgai, A. (2010). Multi-objective location modelling of urban parks and open spaces: Continuous optimization. Computers Environment and Urban Systems, 34(5), 359-376. https://doi.org/10.1016/j.compenvurbsys.2010.03.001
RGoZ. (2007). Zanzibar strategy for growth and reduction of poverty (ZSGRP). Zanzibar.
RGoZ. (2011). Revisited Zanzibar Development Vision 2020. Zanzibar.
Saadatseresht, M., Mansourian, A., & Taleai, M. (2009). Evacuation planning using multiobjective evolutionary optimization approach. European Journal of Operational Research, 198(2009), 305-314. https://doi.org/10.1016/j.ejor.2008.07.032
Saaty, T. L. (1980). The analytic hierarchy process, planning, piority setting, resource allocation. New York: McGraw Hill.
Salim, I. S., & Mwaipopo, L. J. (2016). What satisfies tourists in cultural heritage sites? Evidence of Zanzibar Stone Town. Journal of Research in Hospitality, Tourism and Culture, 3(1), 1-10.
Sharpley, R., & Ussi, M. (2014). tourism and governance in Small Island Developing States (SIDS): The Case of Zanzibar. International Journal of Tourism Research, 16, 87-96. https://doi.org/10.1002/jtr.1904
Shaygan, M., Alimohammadi, A., Mansourian, A., Govara, Z. S., & Kalami, S. M. (2014). Spatial Multi-Objective Optimization Approach for Land Use Allocation using NSGA-II. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(3), 906-916. https://doi.org/10.1109/JSTARS.2013.2280697
Shi, Y., & Eberhart, R. C. (1998). A modified particle swarm optimizer. Paper presented at the Proceedings of IEEE International Conference on Evolutionary Computation. Piscataway, NJ. https://doi.org/10.1109/ICEC.1998.699146
Shifa, M., Jianhua, H. E., Feng, L. I. U., & Yan, Y. U. (2011). Land-use spatial optimization based on PSO algorithm. GeoSpatial Information Science, 14(1), 54-61. https://doi.org/10.1007/s11806-011-0437-8
Sida. (2004). A future for the past: historic cities in development. Stockholm.
Silva, C. N. (2015). Urban planning in Sub-Saharan Africa: colonial and postcolonial planning cultures. New York: Routledge. https://doi.org/10.4324/9781315797311
Sjöstrand, J. (2014). Cultivating authenticity: Perceptions of Zanzibari culture and history within the heritage management of Stone Town (Master’s Thesis in Urban and Regional Planning), Stockholm University, Stockholm.
Srinivas, N., & Deb, K. (1994). Multi-objective optimization using non-dominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221-248. https://doi.org/10.1162/evco.1994.2.3.221
Stewart, T. J., Janssen, R., & Van Herwijnen, M. (2004). A genetic algorithm approach to multiobjective landuse planning. Computers & Operations Research, 31(2004), 2293-2313. https://doi.org/10.1016/S0305-0548(03)00188-6
Ullah, K. M., & Mansourian, A. (2015). Evaluation of land suitability for urban land-use planning: case study Dhaka city. Transaction in GIS. https://doi.org/10.1111/tgis.12137
UN-Habitat. (2010). Planning sustainable cities: UN-habitat practices and perspectives. Nairobi.
UNESCO. (2000). Stone Town of Zanzibar. UNESCO.
Wang, & Li, Y. (2015, 12-14 December). Multi-objective artificial bee colony algorithm. 2015 International Conference on Computational Intelligence and Communication Networks (CICN). Jabalpur, India: IEEE.
Wang, H., Shen, Q., Tang, B., & Skitmore, M. (2013). An integrated approach to supporting land-use decisions in site redevelopment for urban renewal in Hong Kong. Habitat International, 38, 70-80. https://doi.org/10.1016/j.habitatint.2012.09.006
Xu, G., Yang, Y., Liu, B., Xu, Y., & Wu, A. (2015). An efficient hybrid multi-objective particle swarm optimization with a multi-objective dichotomy line search. Journal of Computational and Applied Mathematics, 280(2015), 310-326. https://doi.org/10.1016/j.cam.2014.11.056
Yan, G., & Li, C. (2011). An effective refinement artificial bee colony optimization algorithm based on chaotic search and application for PID control tuning. Journal of Computational Information Systems, 7, 3309-3316.
Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto Approach. IEEE Transactions on Evolutionary Computation, 3(4), 257-271. https://doi.org/10.1109/4235.797969