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Land Use Simulation Models

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Spatial Interaction Models with Land Use

Part of the book series: Contributions to Regional Science ((CRR))

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Abstract

This chapter presents a review of the literature on land use models structured according to disaggregation of space, time and decision-makers and representing the process of land use mosaic and change. Geographical models relate land use to the properties of the land supply, its suitability for different types of use and its location. Economic models assume that land use is determined by the demand for land, influenced by a system of preferences, motivations, markets, accessibility, and population. Agronomic models do not include spatial interaction and have as a fundamental reference each plot of soil, apt to different types of culture, conditioned both by the markets and by the aptitudes of each territory unit to a certain type of culture. Spatial interaction models focus mainly the movement of flows across space constrained by the attrition of distance between origins and destinations. Finally, integrated models, join spatial interaction with land use explicitly including human behaviour in the decision-making processes and diversified land properties.

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Notes

  1. 1.

    Some authors in Portuguese-language literature use the term “land use” and others use the term “land occupation”. In English, there is no such disambiguation, using only the term land use and land cover (referring to soil cover). Land use is “the result of the total combination of activities and inputs that people undertake in a given type of land cover” while land cover refers to the “observed physical and biological cover that covers the earth's surface, such as vegetation or human-induced changes (FAO and UNEP 1999). It is widely used in thematic cartography. A well-known example is the Corine Land Cover—CCL classification, applied mainly in Europe.

  2. 2.

    The first commercial GIS software with a graphical interface was launched in the United States of America in 1982, by the company founded in California in 1969 by Jack and Laura Dangermond under the name of the Environmental Systems Research Institute, Inc (ESRI). This first software was called ArcInfo and worked on the UNIX operating system. Shortly thereafter, the same version for the Windows operating system was launched, being a market success, making the company the market leader in this sector until today, despite the popularity that has been assuming the SIG Open Source software.

  3. 3.

    A spatially representative model, has the ability to incorporate, produce or display spatial data in two or three dimensions. However, it does not have the capacity to model topological relationships and interactions between geographic elements (cells, lines, points and polygons).

  4. 4.

    A spatially interactive model, explicitly defines spatial relationships and their interactions between neighboring entities or cells.

  5. 5.

    Intelligent algorithms was the term adopted in Portuguese for the English name “Machine Learning”. The underlying idea is that machine learning are models developed with logarithms capable of reading databases, without having to resort to programming rules.

  6. 6.

    Public utility services, such as water, electricity and telephone; drainage networks or road networks.

  7. 7.

    Models with alpha-numeric information, such as altimetry grids.

  8. 8.

    Aerial photographs, satellite images, maps, which can be georeferenced or not.

  9. 9.

    The protected landscape of the Biscoitos vineyard is located in a small coastal area (165 ha) north of Terceira island, consisting of a coastal area of biscuit (basaltic stone), with land compartmentalized by small plots bounded by basalt rock walls (protecting the vineyards from the abrasive action of the sea and the weather), for the production of verdelho wine, which constitute a rural architecture built since the settlement of the island of Terceira. In recent decades, urban pressure on the coastal zone of Biscoitos has created a threat to the landscape maintenance of this area, which has led to its classification and regulation of its use.

  10. 10.

    Commodities—These are products or goods that are indispensable in today's societies. They are traded daily on a global scale and their price is usually determined by the international market, varying according to supply and demand, such as oil or cereals (Schumpeter 1934).

  11. 11.

    Bottom-up structure—The behavior of agents is modeled at the regional level. A totally interdependent system is specified, so that national-regional retroactivities can be considered in both directions. In this way, the analysis of policies that originate at the regional level is facilitated. The results obtained at national level result from the aggregation of regional results (Haddad, 2011).

References

  • Abdullahi, S., & Pradhan, B. (2016). Sustainable Brownfields Land Use Change Modeling Using GIS-based Weights-of-Evidence Approach. Applied Spatial Analysis and Policy, 9(1), 21–38. doi: https://doi.org/10.1007/s12061-015-9139-1

    Article  Google Scholar 

  • Adams, D. M., Alig, R. J., Callaway, J. M., Winnett, S. M., & McCarl, B. A. (1996). The Forest and Agricultural Sector Optimization Model (FASOM): Model Structure and Policy Applications: Diane Publishing Company.

    Google Scholar 

  • Agarwal, C., Green, G. L., Grove, M., Evans, T., & Schweik, C. (2000). A Review and Assessment of Land-Use Change Models - Dynamics of Space, Time, and Human Choice. Bloomington - Indiana: Center for the Study of Institutions Population, and Environmental Change Indiana University.

    Google Scholar 

  • Agarwal, C., Green, G. M., Grove, J. M., Evans, T. P., & Schweik, C. M. (2002). A review and assessment of land-use change models: dynamics of space, time, and human choice.

    Google Scholar 

  • Ahmed, T., Ahmad, B., & Ahmad, W. (2015). Why do farmers burn rice residue? Examining farmers’ choices in Punjab, Pakistan. Land Use Policy, 47, 448–458. doi: https://doi.org/10.1016/j.landusepol.2015.05.004

    Article  Google Scholar 

  • Alcamo, J., Kreileman, E., Krol, M., Leemans, R., Bollen, J., Van Minnen, J., . . . De Vries, B. (1998). Global modelling of environmental change: an overview of IMAGE 2.1. Global change scenarios of the 21st century. Results from the IMAGE, 2(1), 3–94.

    Google Scholar 

  • Alonso, W. (1964). Location and Land Use. Cambridge-MA: Harvard University Press.

    Book  Google Scholar 

  • Anselme, B., Bousquet, F., Lyet, A., Etienne, M., Fady, B., & Le Page, C. (2010). Modelling of spatial dynamics and biodiversity conservation on Lure mountain (France). Environmental Modelling & Software, 25(11), 1385–1398. doi: https://doi.org/10.1016/j.envsoft.2009.09.001

    Article  Google Scholar 

  • Baker, W. L. (1989). A review of models of landscape change. Landscape Ecology, 2(2), 111–133. doi: https://doi.org/10.1007/bf00137155

    Article  Google Scholar 

  • Balzter H, Braun P W, Kohler W (1998) Cellular automata models for vegetation dynamics, Ecological Modelling 107(2/3):113–25.

    Article  Google Scholar 

  • Batty, M., Crooks, A. T., See, L. M., & Heppenstall, A. J. (2012). Perspectives on Agent-Based Models and Geographical Systems. In A. J. Heppenstall, A. T. Crooks, L. M. See & M. Batty (Eds.), Agent-Based Models of Geographical Systems (pp. 1-15). Dordrecht: Springer Netherlands.

    Google Scholar 

  • Benítez, P. C., & Obersteiner, M. (2006). Site identification for carbon sequestration in Latin America: A grid-based economic approach. Forest Policy and Economics, 8(6), 636–651. doi: https://doi.org/10.1016/j.forpol.2004.12.003

    Article  Google Scholar 

  • Blikstein, M. (1982). Modelos matemáticos no planejamento de transportes urbanos: uma abordagem crítica. Fundação Gertúlio Vargas, São Paulo.

    Google Scholar 

  • Briassoulis, H. (2000). Analysis of Land Use Change: Theoretical and Modeling Approaches: Regional Research Institute, West Virginia University.

    Google Scholar 

  • Brooks, J., & Dewbre, J. (2006). Global trade reforms and income distribution in developing countries. Journal of Agricultural and Development Economics, 3(1), 86–111.

    Google Scholar 

  • Burniaux, J. M., & Lee, H.-L. (2003). Modelling Land Use Changes in GTAP West Lafayette, IN, USA: Center for Global Trade Analysis Project.

    Google Scholar 

  • Burrough, P. A. (1986). Principles of Geographic Information Systems for Land Resources Assessment. Oxford: Oxford University Press.

    Google Scholar 

  • Burrough, P. A., McDonnell, R. A., McDonnell, R., & Lloyd, C. D. (2015). Principles of geographical information systems: Oxford University Press.

    Google Scholar 

  • Buurman, J., Rietveld, P., & Scholten, H. (2001). The Land Market: A Spatial Economic Perspective. In J. Stillwell & H. Scholten (Eds.), Land Use Simulation for Europe (pp. 65–82). Dordrecht: Springer Netherlands.

    Chapter  Google Scholar 

  • Champion, A., Bramley, G., Fotheringham, A. S., Macgill, J., & Rees, P. (2003). A migration modelling system to support government decision-making. In S. Geertman & J. C. H. Stillwell (Eds.), Planning Support Systems in Practice (pp. 266–290). Berlin: Springer.

    Google Scholar 

  • Chang-Martínez, A. L., Mas, J.-F., Valle, T. N., Torres, S. P., & Folan, J. W. (2015). Modeling Historical Land Cover and Land Use: A Review from Contemporary Modeling. ISPRS International Journal of Geo-Information, 4(4). doi: https://doi.org/10.3390/ijgi4041791

  • Clarke, K. and Gaydos, L. (1998) Loose-Coupling a Cellular Automaton Model and GIS: Long-Term Urban Growth Prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science, 12, 699–714.

    Article  Google Scholar 

  • Committee, G. S. (2014). Advancing land change modeling: opportunities and research requirements: National Academies Press.

    Google Scholar 

  • Costa, J. P. (2010). Criação de cenários de expansão urbana com recurso a autómatos celulares no apoio ao planeamento e gestão territorial: Cabeceiras de Basto (Norte de Portugal). Msc, Universidade do Porto, Porto.

    Google Scholar 

  • Couclelis, H. (1997). From Cellular Automata to Urban Models: New Principles for Model Development and Implementation. Environment and Planning B: Planning and Design, 24(2), 165–174. https://doi.org/10.1068/b240165

    Article  Google Scholar 

  • Darwin, R. (1999). A FARMer's View of the Ricardian Approach to Measuring Agricultural Effects of Climatic Change. Climatic Change, 41(3), 371–411. doi: https://doi.org/10.1023/a:1005421707801

    Article  Google Scholar 

  • de Koning, G. H. J., Verburg, P. H., Veldkamp, A., & Fresco, L. O. (1999). Multi-scale modelling of land use change dynamics in Ecuador. Agricultural Systems, 61(2), 77–93. doi: https://doi.org/10.1016/S0308-521X(99)00039-6

    Article  Google Scholar 

  • Demaris, A. (1992). Logit Modeling. Retrieved from http://methods.sagepub.com/book/logit-modeling. doi: https://doi.org/10.4135/9781412984836

  • Deng, X., Liu, J., Lin, Y., & Shi, C. (2013). A Framework for the Land Use Change Dynamics Model Compatible with RCMs. Advances in Meteorology, 2013, 7. doi: https://doi.org/10.1155/2013/658941

  • Deschênes, O., & Greenstone, M. (2007). The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather. [https://doi.org/10.1257/aer.97.1.354]. American Economic Review, 97(1), 354–385.

  • Dorsey, B. (1999). Agricultural Intensification, Diversification, and Commercial Production among Smallholder Coffee Growers in Central Kenya*. Economic Geography, 75(2), 178–195. doi: https://doi.org/10.1111/j.1944-8287.1999.tb00122.x

    Article  Google Scholar 

  • Dubey, V. (1964). The definition of regional economics. Journal of Regional Science, 5(2), 25–29.

    Article  Google Scholar 

  • Echenique, M., Grinevich, V., Hargreaves, A., & Zachariadis, V. (2015). A Land Use and Spatial Interaction Model based on Random Utility Theory and Social Accounting Matrices Cambridge - UK: The Martin Centre, Department of Architecture - University of Cambridge.

    Google Scholar 

  • Eickhout, B., van Meijl, H., Tabeau, A., & van Rheenen, T. (2007). Economic and ecological consequences of four European land use scenarios. Land Use Policy, 24(3), 562–575. doi: https://doi.org/10.1016/j.landusepol.2006.01.004

    Article  Google Scholar 

  • Engelen, G., Geertman, S., Smits, P., & Wessels, C. (1999). Dynamic GIS and strategic physical planning support: a practical application. Geographical Information and Planning: European Perspectives, 87–111.

    Google Scholar 

  • FAO & UNEP. (1999). Terminology for Integrated Resources Planning and Management. Rome, Italy

    Google Scholar 

  • FAO. (1997). Land Quality Indicators and Their Use in Sustainable Agriculture and Rural Development: Proceedings of the Workshop. Paper presented at the Fao Land and Water Bulletin.

    Google Scholar 

  • Fotheringham, A. S. (1991). Migration and spatial structure; the development of the competing destinations model. In J. C. H. Stillwell & P. Congdon (Eds.), Migration Models Macro and Micro Approaches (pp. 57–72). London: Belhaven Press.

    Google Scholar 

  • Geoghegan, J., Schneider, L., & Vance, C. (2004). Temporal dynamics and spatial scales: Modeling deforestation in the southern Yucatán peninsular region. GeoJournal, 61(4), 353–363. doi: https://doi.org/10.1007/s10708-004-5052-x

    Article  Google Scholar 

  • Ginsburgh, V., & Keyzer, M. (1997). The Structure of Applied General Equilibrium Models. Cambridge, USA/London: MIT Press.

    Google Scholar 

  • Golub, A., Hertel, T. W., & Sohngen, B. (2009). 10 Land use modelling in a recursively dynamic GTAP framework. Economic analysis of land use in global climate change policy, 14, 235.

    Google Scholar 

  • Golub, A. A., Henderson, B. B., Hertel, T. W., Gerber, P. J., Rose, S. K., & Sohngen, B. (2013). Global climate policy impacts on livestock, land use, livelihoods, and food security. Proceedings of the National Academy of Sciences of the United States of America, 110(52), 20894–20899. doi: https://doi.org/10.1073/pnas.1108772109

    Article  Google Scholar 

  • Goodchild, M. F. (1992). Geographical information science. International Journal of Geographical Information Systems, 6(1), 31–45. doi: https://doi.org/10.1080/02693799208901893

    Article  Google Scholar 

  • Griffith, D. A. (2012). Advanced spatial statistics: special topics in the exploration of quantitative spatial data series (Vol. 12): Springer Science & Business Media.

    Google Scholar 

  • Gusti, M., Havlik , P., & Obersteiner, M. (2008). Technical description of the IIASA model cluster. 12. Retrieved from http://digital.library.unt.edu/ark:/67531/metadc13707/

  • Haddad, E. (2011). Modelos Computacionais de Equilíbrio Geral Inter-Regionais. In J. S. Costa, T. P. Dentinho & P. Nijkamp (Eds.), Compêndio de Economia Regional (Vol. II, pp. 563–607). Cascais: Principia.

    Google Scholar 

  • Havlík, P., Schneider, U. A., Schmid, E., Böttcher, H., Fritz, S., Skalský, R., . . . Obersteiner, M. (2011). Global land-use implications of first and second generation biofuel targets. Energy Policy, 39(10), 5690–5702. doi: https://doi.org/10.1016/j.enpol.2010.03.030

  • Haynes, K. E., & Fotheringham, A. S. (1984). Gravity and Spatial Interaction Models. Beverly Hills - USA: Sage Publications.

    Google Scholar 

  • Heistermann, M., Müller, C., & Ronneberger, K. (2006). Land in sight? Achievements, deficits and potentials of continental to global scale land-use modeling. Agriculture, Ecosystems & Environment, 114(2–4), 141–158. doi: https://doi.org/10.1016/j.agee.2005.11.015

    Article  Google Scholar 

  • Hertel, T. W. (1997). Global Trade Analysis Modelling and Applications. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Hertel, T. W., Rose, S., & Tol, R. S. (2009). 1 Land use in computable general equilibrium models. Economic analysis of land use in global climate change policy, 14, 1.

    Google Scholar 

  • Hertel, T. W. (1999). Applied general equilibrium analysis of agricultural and resource policies. Staff paper 99–2. Department of Agricultural Economics, Purdue University - West Lafayette, Indiana.

    Google Scholar 

  • Hibbard, K., Janetos, A., van Vuuren, D. P., Pongratz, J., Rose, S. K., Betts, R., . . . Feddema, J. J. (2010). Research priorities in land use and land-cover change for the Earth system and integrated assessment modelling. International Journal of Climatology, 30(13), 2118–2128. doi: https://doi.org/10.1002/joc.2150

  • Hsin, H., van Tongeren, F., Dewbre, J., & van Meijl, H. (2004). A new representation of agricultural production technology in GTAP. Paper presented at the 7th Annual Conference on Global Economic Analysis, Washington, DC.

    Google Scholar 

  • Irwin, E. G., & Bockstael, N. E. (2004). Land use externalities, open space preservation, and urban sprawl. Regional Science and Urban Economics, 34(6), 705–725. doi: https://doi.org/10.1016/j.regsciurbeco.2004.03.002

    Article  Google Scholar 

  • Irwin, E. G., & Geoghegan, J. (2001). Theory, data, methods: developing spatially explicit economic models of land use change. Agriculture, Ecosystems & Environment, 85(1–3), 7–24. doi: https://doi.org/10.1016/S0167-8809(01)00200-6

    Article  Google Scholar 

  • Jeuffroy, M.-H., Casadebaig, P., Debaeke, P., Loyce, C., & Meynard, J.-M. (2014). Agronomic model uses to predict cultivar performance in various environments and cropping systems. A review. Agronomy for Sustainable Development, 34(1), 121–137. doi: https://doi.org/10.1007/s13593-013-0170-9

    Article  Google Scholar 

  • Jianchu, X., Xihui, A., & Xiqing, D. (2005). Exploring the spatial and temporal dynamics of land use in Xizhuang watershed of Yunnan, southwest China. International Journal of Applied Earth Observation and Geoinformation, 7(4), 299–309. doi: https://doi.org/10.1016/j.jag.2005.06.008

    Article  Google Scholar 

  • Jjumba, A., & Dragićević, S. (2012). High Resolution Urban Land-use Change Modeling: Agent iCity Approach. Applied Spatial Analysis and Policy, 5(4), 291–315. doi: https://doi.org/10.1007/s12061-011-9071-y

    Article  Google Scholar 

  • Joshi, C., Leeuw, J. D., Skidmore, A. K., Duren, I. C., & van Oosten, H. (2006). Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods. International Journal of Applied Earth Observation and Geoinformation, 8(2), 84–95. doi: https://doi.org/10.1016/j.jag.2005.08.004

    Article  Google Scholar 

  • Kamau, S. W., Kuria, D., & Gachari, M. K. (2015). Crop-land Suitability Analysis Using GIS and Remote Sensing in Nyandarua County, Kenya. Journal of Environment and Earth Science, 5(6), 121–132.

    Google Scholar 

  • Kanaroglou, P., & Scott, D. (2002). Integrated urban transportation and land-use models for policy analysis. In M. Dijst, W. Schenkel & I. Thomas (Eds.), Governing Cities on the Move: Functional and Management Perspectives on Transformations of European Urban Infrastructures (pp. 42–72). Aldershot, UK: Ashgate.

    Google Scholar 

  • Keeney, R., & Hertel, T. W. (2005). GTAP-AGR: A Framework for Assessing The Implications of Multilateral Changes in Agricultural Policies GTAP Technical Paper No. 24. West Lafayette, IN, USA: Purdue University.

    Google Scholar 

  • Kim, S. H., Edmonds, J., Lurz, J., Smith, J. S., & Marshall, W. (2006). The ObjECTS framework for integrated assessment: Hybrid modelling of transportation. The Energy Journal, 27, 63–91.

    Article  Google Scholar 

  • Kindermann, G. E., McCallum, I., Fritz, S., & Obersteiner, M. (2008). A global forest growing stock, biomass and carbon map based on FAO statistics. Silva Fennica, 42, 387–396.

    Article  Google Scholar 

  • Klosterman, R. E., & Pettit, C. J. (2005). An Update on Planning Support Systems. Environment and Planning B: Planning and Design, 32(4), 477–484. doi: https://doi.org/10.1068/b3204ed

    Article  Google Scholar 

  • Kockelman, K. M., Jin, L., Zhao, Y., & Ruíz-Juri, N. (2005). Tracking land use, transport, and industrial production using random-utility-based multiregional input–output models: Applications for Texas trade. Journal of Transport Geography, 13(3), 275–286. doi: https://doi.org/10.1016/j.jtrangeo.2004.04.009

    Article  Google Scholar 

  • Koomen, E., & Stillwell, J. (2007). Modelling Land-Use Change. In E. Koomen, J. Stillwell, A. Bakema & H. J. Scholten (Eds.), Modelling Land-Use Change: Progress and Applications (pp. 1–22). Dordrecht: Springer Netherlands.

    Google Scholar 

  • Koomen, E., Koekoek, A., & Dijk, E. (2011). Simulating Land-use Change in a Regional Planning Context. Applied Spatial Analysis and Policy, 4(4), 223–247. doi: https://doi.org/10.1007/s12061-010-9053-5

    Article  Google Scholar 

  • Kuhn, A. (2003). From World Market to Trade Flow Modelling - The Re-Designed WATSIM Model. Bonn: Institute of Agricultural Policy, Market Research and Economic Sociology.

    Google Scholar 

  • Lambin, E. F., & Meyfroidt, P. (2010). Land use transitions: Socio-ecological feedback versus socio-economic change. Land Use Policy, 27(2), 108–118. doi: https://doi.org/10.1016/j.landusepol.2009.09.003

    Article  Google Scholar 

  • Lambin, E. F., Rounsevell, M. D. A., & Geist, H. J. (2000). Are agricultural land-use models able to predict changes in land-use intensity? Agriculture, Ecosystems & Environment, 82(1–3), 321–331. doi: https://doi.org/10.1016/S0167-8809(00)00235-8

    Article  Google Scholar 

  • Laney, R. M. (2004). A process-led approach to modeling land change in agricultural landscapes: a case study from Madagascar. Agriculture, Ecosystems & Environment, 101(2–3), 135–153. doi: https://doi.org/10.1016/j.agee.2003.09.004

    Article  Google Scholar 

  • Lavorel, S., Davies, I. D., & Noble, I. R. (2000). LAMOS: a LAndscape MOdelling Shell. LANDSCAPE FIRE MODELING-CHALLENGES AND OPPORTUNITIES.

    Google Scholar 

  • Leimbach, M., Popp, A., Lotze-Campen, H., Bauer, N., Dietrich, J. P., & Klein, D. (2011). Integrated Assessment Models – the Interplay of Climate Change, Agriculture and Land Use in a Policy Tool Handbook on Climate Change and Agriculture. Cheltenham, UK: ‘Edward Elgar Publishing, Inc.‘.

    Google Scholar 

  • Leimbach, M., & Toth, F. L. (2003). Economic Development and Emission Control over the Long Term: The ICLIPS Aggregated Economic Model. Climatic Change, 56(1), 139–165. doi: https://doi.org/10.1023/a:1021392530774

    Article  Google Scholar 

  • Li, H., & Reynolds, J. F. (2023). Modeling effects of spatial pattern, drought, and grazing on rates of rangeland degradation: A combined Markov and cellular automaton approach. In Scale in remote sensing and GIS (pp. 211–230). Routledge.

    Google Scholar 

  • Liao, T. (1994). Interpreting Probability Models. Retrieved from http://methods.sagepub.com/book/interpreting-probability-models. doi: https://doi.org/10.4135/9781412984577

  • Lowry, I. S. (1964). A model of metropolis rm-4035-rc. Rand Corporation, Santa Monica, CA.

    Google Scholar 

  • Luz, L. (2003). Análise da aptidão cultural de Timor-Leste utilizando um Sistema de Informação Geográfica. Universidade Técnica de Lisboa, Lisboa.

    Google Scholar 

  • Massetti, E., & Mendelsohn, R. (2011). Estimating Ricardian Models with Panel Data.

    Google Scholar 

  • Matsuoka, Y., Morita, T., & Kainuma, M. (2001). Integrated assessment model of climate change: The AIM approach. In T. Matsuno & H. Kida (Eds.), Present and Future of Modelling Environmental Change: Toward Integrated Modelling (pp. 458). Tokyo, Japan: Terra Scientific Publishing Company.

    Google Scholar 

  • Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G., & Gotts, N. M. (2007). Agent-based land-use models: a review of applications. Landscape Ecology, 22(10), 1447–1459. doi: https://doi.org/10.1007/s10980-007-9135-1

    Article  Google Scholar 

  • Meiyappan, P., Dalton, M., O’Neill, B. C., & Jain, A. K. (2014). Spatial modeling of agricultural land use change at global scale. Ecological Modelling, 291, 152–174. doi: https://doi.org/10.1016/j.ecolmodel.2014.07.027

    Article  Google Scholar 

  • Mendelsohn, R., & Nordhaus, W. D. (1999). The Impact of Global Warming on Agriculture: A Ricardian Analysis: Reply. [https://doi.org/10.1257/aer.89.4.1046]. American Economic Review, 89(4), 1046–1048.

  • Mertens, B., & Lambin, E. F. (2000). Land-Cover-Change Trajectories in Southern Cameroon. Annals of the Association of American Geographers, 90(3), 467–494. doi: https://doi.org/10.1111/0004-5608.00205

    Article  Google Scholar 

  • Miao, L., Zhu, F., Sun, Z., Moore, C. J., & Cui, X. (2016). China’s Land-Use Changes during the Past 300 Years: A Historical Perspective. International Journal of Environmental Research and Public Health, 13(9). doi: https://doi.org/10.3390/ijerph13090847

  • Michetti, M., & Parrado, R. (2012). Improving land-use modelling within CGE to assess forest-based mitigation potential and costs. Paper presented at the Towards a Sustainable Bio-economy: Economic Issues and Policy Challenges, Trento.

    Google Scholar 

  • Michetti, M., & Zampieri, M. (2014). Climate–Human–Land Interactions: A Review of Major Modelling Approaches. Land, 3(3). doi: https://doi.org/10.3390/land3030793

  • Moeckel, R., Schürmann, C., & Wegener, M. (2002). Microsimulation of urban land use. Paper presented at the 42nd European Congress of the Regional Science Association, Dortmund.

    Google Scholar 

  • Mulley, B. G., & Unruh, J. D. (2004). The role of off-farm employment in tropical forest conservation: labor, migration, and smallholder attitudes toward land in western Uganda. Journal of Environmental Management, 71(3), 193–205. doi: https://doi.org/10.1016/j.jenvman.2004.02.002

    Article  Google Scholar 

  • Oliver, T. H., & Morecroft, M. D. (2014). Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities. Wiley Interdisciplinary Reviews: Climate Change, 5(3), 317–335. doi: https://doi.org/10.1002/wcc.271

    Article  Google Scholar 

  • O'Sullivan. (2009). Urban Economics (8th ed. ed.): McGraw-Hill/Irwin.

    Google Scholar 

  • Paltsev, S., Reilly, J. M., Jacoby, H. D., Eckaus, R. S., McFarland, J., Sarofim, M., . . . Babiker, M. (2005). Emissions Prediction and Policy Analysis (EPPA) Model: Version 4 Report No. 125; MIT Joint Program for the Science and Policy of Global Change. Cambridge, MA, USA: Massachusetts Institute of Technology.

    Google Scholar 

  • Van Passel, S., Massetti, E., & Mendelsohn, R. (2016). A Ricardian Analysis of the Impact of Climate Change on European Agriculture. Environmental and Resource Economics, 1–36. doi: https://doi.org/10.1007/s10640-016-0001-y

  • Pfaff, A. S. P. (1999). What Drives Deforestation in the Brazilian Amazon? Journal of Environmental Economics and Management, 37(1), 26–43. doi: https://doi.org/10.1006/jeem.1998.1056

    Article  Google Scholar 

  • Pielke, R. A. (2013). Climate Vulnerability, Understanding and Addressing Threats to Essential Resources (1st ed. ed.). Amsterdam, The Netherlands/Boston, MA, USA: Academic Press.

    Google Scholar 

  • Poelmans, L., & Van Rompaey, A. (2009). Detecting and modelling spatial patterns of urban sprawl in highly fragmented areas: A case study in the Flanders–Brussels region. Landscape and urban planning, 93(1), 10–19.

    Article  Google Scholar 

  • Porteiro, J., Calado, H., Pereira, M., & Dentinho, T. (2005). Integrated Management on the Sete Cidades WaterShed. Paper presented at the 14th European Colloquium on Theoretical and Quantitative Geography, Tomar - Portugal.

    Google Scholar 

  • Reilly, W. J. (1931). The Law of Retail Gravitation. New York: Pilsbury.

    Google Scholar 

  • Ricardo, D. (1817). The Principles of Political Economy and Taxation (Vol. The Works and Correspondence of David Ricardo). New York: Cambridge University Press.

    Google Scholar 

  • Ronneberger, K., Berrittella, M., Bosello, F., & Tol, R. S. (2008). Klum@ gtap: Spatially-explicit, biophysical land use in a computable general equilibrium model. This volume.

    Google Scholar 

  • Ronneberger, K. (2006). The global agricultural land-use model KLUM Reports on Earth System Science: Max Planck Institute for Meteorology.

    Google Scholar 

  • Rose, S., Ahammad, H., Eickhout, B., Fisher, B., Kurosawa, A., Rao, S., . . . van Vuuren, D. (2008). Land in Climate Stabilization Modeling: Initial Observations Energy Modeling Forum. Stanford, CA: Stanford University.

    Google Scholar 

  • Rosegrant, M. W., Cai, X., & Cline, S. A. (2002). World water and food to 2025: dealing with scarcity. Washington, D.C.: International Food Policy Research Institute.

    Google Scholar 

  • Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J., Thorburn, P., . . . Winter, J. M. (2013). The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies. Agricultural and Forest Meteorology, 170, 166–182. doi: https://doi.org/10.1016/j.agrformet.2012.09.011

  • Rounsevell, M. D. A., Annetts, J. E., Audsley, E., Mayr, T., & Reginster, I. (2003). Modelling the spatial distribution of agricultural land use at the regional scale. Agriculture, Ecosystems & Environment, 95(2–3), 465–479. doi: https://doi.org/10.1016/S0167-8809(02)00217-7

    Article  Google Scholar 

  • Rounsevell, M. D. A., Reginster, I., Araújo, M. B., Carter, T. R., Dendoncker, N., Ewert, F., . . . Tuck, G. (2006). A coherent set of future land use change scenarios for Europe. Agriculture, Ecosystems & Environment, 114(1), 57–68. doi: https://doi.org/10.1016/j.agee.2005.11.027

  • Rounsevell, M. D. A., Pedroli, B., Erb, K.-H., Gramberger, M., Busck, A. G., Haberl, H., . . . Wolfslehner, B. (2012). Challenges for land system science. Land Use Policy, 29(4), 899–910. doi: https://doi.org/10.1016/j.landusepol.2012.01.007

  • Sands, R. D., & Edmonds, J. A. (2005). Climate Change Impacts for the Conterminous USA: An Integrated Assessment. Climatic Change, 69(1), 127–150. doi: https://doi.org/10.1007/s10584-005-3616-5

    Article  Google Scholar 

  • Sanghi, A., & Mendelsohn, R. (2008). The impacts of global warming on farmers in Brazil and India. Global Environmental Change, 18(4), 655–665. doi: https://doi.org/10.1016/j.gloenvcha.2008.06.008

    Article  Google Scholar 

  • Saseendran, S. A., Nielsen, D. C., Ma, L., Ahuja, L. R., & Halvorson, A. D. (2004). Modeling Nitrogen Management Effects on Winter Wheat Production Using RZWQM and CERES-Wheat. Agronomy Journal, 96(3), 615–630. doi: https://doi.org/10.2134/agronj2004.0615

    Article  Google Scholar 

  • Saujot, M., Lapparent , M., Arnaud, E., & Prados, E. (2015). To make LUTI models operational tools for planning: Report TRANSP-OR 150310. Lausanne: Ecole Polytechnique Fédérale de Lausanne.

    Google Scholar 

  • Schlosser, C. A., Kicklighter, D., & Sokolov, A. (2007). A global land system framework for integrated climate-change assessments: MIT Joint Program on the Science and Policy of Global Change.

    Google Scholar 

  • van Schrojenstein Lantman, J., Verburg, P. H., Bregt, A., & Geertman, S. (2011). Core Principles and Concepts in Land-Use Modelling: A Literature Review. In E. Koomen & J. Borsboom-van Beurden (Eds.), Land-Use Modelling in Planning Practice (pp. 35–57). Dordrecht: Springer Netherlands.

    Google Scholar 

  • Schumpeter, J. A. (1934). The theory of economic development: An inquiry into profits, capital, credit, interest, and the business cycle (Vol. 55): Transaction publishers.

    Google Scholar 

  • Silveira, P., & Dentinho, T. (2010). Spatial interaction model of land use - An application to Corvo Island from the 16th, 19th and 20th centuries. Computers Environment and Urban Systems, 34(2), 91-103. doi: https://doi.org/10.1016/j.compenvurbsys.2009.10.003

    Article  Google Scholar 

  • Silveira, P. (2009). Aplicação de um modelo de interação espacial no estudo da evolução do uso do solo ao longo do século XX na Ilha Terceira. Ms, Universidade dos Açores, Angra do Heroísmo.

    Google Scholar 

  • Sohngen, B., & Mendelsohn, R. (2007). A sensitivity analysis of forest carbon sequestration. In M. E. Schlesinger, H. S. Kheshgi, J. Smith, F. C. de la Chesnaye, J. M. Reilly, T. Wilson & C. Kolstad (Eds.), Human-Induced Climate Change: An Interdisciplinary Assessment (pp. 227–237). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Sokolov, A. P., Schlosser, C. A., Dutkiewicz, S., Paltsev, S., Kicklighter, D. W., Jacoby, H. D., . . . Wang, C. (2005). The MIT Integrated Global System Model (IGSM) Version 2: Model Description and Baseline Evaluation MIT Joint Program on the Science and Policy of Global Change. Cambridge, MA, USA: MIT.

    Google Scholar 

  • Stéphenne, N., & Lambin, E. F. (2001). A dynamic simulation model of land-use changes in Sudano-sahelian countries of Africa (SALU). Agriculture, Ecosystems & Environment, 85(1–3), 145–161. doi: https://doi.org/10.1016/S0167-8809(01)00181-5

    Article  Google Scholar 

  • Stephenne, N., & Lambin, E. F. (2004). Scenarios of land-use change in Sudano-sahelian countries of Africa to better understand driving forces. GeoJournal, 61(4), 365–379. doi: https://doi.org/10.1007/s10708-004-5053-9

    Article  Google Scholar 

  • Stillwell, J. C. H. (1991). Spatial interaction models and the propensity to migrate over distance. In J. C. H. Stillwell & P. Congdon (Eds.), Migration Models Macro and Micro Approaches (pp. 34–36). London: Belhaven Press.

    Google Scholar 

  • Stöckle, C. O., Donatelli, M., & Nelson, R. (2003). CropSyst, a cropping systems simulation model. European Journal of Agronomy, 18(3–4), 289–307. doi: https://doi.org/10.1016/S1161-0301(02)00109-0

    Article  Google Scholar 

  • Tan, G. X., Shibasaki, R., Matsumura, K., & Rajan, K. S. (2003). Global research for integrated agricultural land use change modelling. Paper presented at the Asia GIS Conference 2003 Publications, Wuhan - China.

    Google Scholar 

  • Tavoni, M., Sohngen, B., & Bosetti, V. (2007). Forestry and the carbon market response to stabilize climate. Energy Policy, 35(11), 5346–5353. doi: https://doi.org/10.1016/j.enpol.2006.01.036

    Article  Google Scholar 

  • Thornton, P. K., & Jones, P. G. (1998). A conceptual approach to dynamic agricultural land-use modelling. Agricultural Systems, 57(4), 505–521. doi: https://doi.org/10.1016/S0308-521X(98)00005-5

    Article  Google Scholar 

  • Timmermans, H. (2003). The saga of integrated land use-transport modeling: How many more dreams before we wake up? Paper presented at the 10th International Conference on Travel Behaviour Research, Lucerne.

    Google Scholar 

  • Tobler, W. R. (1970). “A Computer Movie Simulating Urban Growth in the Detroit Region”. Economic Geography. 46: 234. doi:https://doi.org/10.2307/143141. ISSN 0013–0095. JSTOR 143141. Retrieved 2023-10-02.

    Article  Google Scholar 

  • Tomppo, E., Nilsson, M., Rosengren, M., Aalto, P., & Kennedy, P. (2002). Simultaneous use of Landsat-TM and IRS-1C WiFS data in estimating large area tree stem volume and aboveground biomass. Remote Sensing of Environment, 82(1), 156–171. doi: https://doi.org/10.1016/S0034-4257(02)00031-7

    Article  Google Scholar 

  • Toppinen, A., & Kuuluvainen, J. (2010). Forest sector modelling in Europe—the state of the art and future research directions. Forest Policy and Economics, 12(1), 2–8. doi: https://doi.org/10.1016/j.forpol.2009.09.017

    Article  Google Scholar 

  • Toth, F. L., Bruckner, T., Füssel, H. M., Leimbach, M., & Petschel-Held, G. (2003). Integrated Assessment of Long-term Climate Policies: Part 1 – Model Presentation. Climatic Change, 56(1), 37–56. doi: https://doi.org/10.1023/a:1021332312119

    Article  Google Scholar 

  • Tubiello, F. N., & Fischer, G. (2007). Reducing climate change impacts on agriculture: Global and regional effects of mitigation, 2000–2080. Technological Forecasting and Social Change, 74(7), 1030–1056. doi: https://doi.org/10.1016/j.techfore.2006.05.027

    Article  Google Scholar 

  • Varela, L. M., Rotundo, G., Ausloos, M., & Carrete, J. (2015). Complex Network Analysis in Socioeconomic Models. In P. Commendatore, S. Kayam & I. Kubin (Eds.), Complexity and Geographical Economics: Topics and Tools (pp. 209–245). Cham: Springer International Publishing.

    Chapter  Google Scholar 

  • Veldkamp, A., & Fresco, L. O. (1996). CLUE-CR: An integrated multi-scale model to simulate land use change scenarios in Costa Rica. Ecological Modelling, 91(1), 231–248. doi: https://doi.org/10.1016/0304-3800(95)00158-1

    Article  Google Scholar 

  • Verburg, P. H., & Overmars, K. P. (2009). Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecology, 24(9), 1167–1181. doi: https://doi.org/10.1007/s10980-009-9355-7

    Article  Google Scholar 

  • Verburg, P. H., Veldkamp, A., & Fresco, L. O. (1999). Simulation of changes in the spatial pattern of land use in China. Applied Geography, 19(3), 211–233. doi: https://doi.org/10.1016/S0143-6228(99)00003-X

    Article  Google Scholar 

  • Verburg, H. P., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., & Mastura, S. A. S. (2002). Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model. Environmental Management, 30(3), 391–405. doi: https://doi.org/10.1007/s00267-002-2630-x

    Article  Google Scholar 

  • Verburg, P. H., van Eck, J. R. R., de Nijs, T. C. M., Dijst, M. J., & Schot, P. (2004a). Determinants of land-use change patterns in the Netherlands. Environment and Planning B: Planning and Design, 31(1), 125–150.

    Article  Google Scholar 

  • Verburg, P. H., Schot, P. P., Dijst, M. J., & Veldkamp, A. (2004b). Land use change modelling: current practice and research priorities. GeoJournal, 61(4), 309–324. doi: https://doi.org/10.1007/s10708-004-4946-y

    Article  Google Scholar 

  • von Thünen, J. H. (1826). Isolated State: An English edition of Der isolierte Staat. Oxford: Pergamon Press, 1966.

    Google Scholar 

  • Wagner, P., & Wegener, M. (2007). Urban Land Use, Transport and Environment Models: Experiences with an Integrated Microscopic Approach. disP - The Planning Review, 43(170), 45–56. doi: https://doi.org/10.1080/02513625.2007.10556988

  • Wassenaar, T., Gerber, P., Verburg, P. H., Rosales, M., Ibrahim, M., & Steinfeld, H. (2007). Projecting land use changes in the Neotropics: The geography of pasture expansion into forest. Global Environmental Change, 17(1), 86–104. doi: https://doi.org/10.1016/j.gloenvcha.2006.03.007

    Article  Google Scholar 

  • Wegener, M. (1998). Applied models of urban land use, transport and environment: state of the art and future developments. In L. Lundqvist, L. G. Mattson & T. J. Kim (Eds.), Network Infrastructure and the Urban Environment: Recent Advances in Land use/Transportation Modelling (pp. 245–267). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Wegener, M. (2014). Land-Use Transport Interaction Models. In M. M. Fischer & P. Nijkamp (Eds.), Handbook of Regional Science (pp. 741–758). Berlin, Heidelberg: Springer Berlin Heidelberg.

    Google Scholar 

  • White, R., & Engelen, G. (2000). High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computers, environment and urban systems, 24(5), 383–400

    Article  Google Scholar 

  • Wilson, A. G. (1970). Entropy in Urban and Regional Modelling. London: Pion.

    Google Scholar 

  • Wilson, A. G. (1998). Land-use/transport interaction models: Past and future. Journal of transport economics and policy, 3–26.

    Google Scholar 

  • Wood, E. C., Tappan, G. G., & Hadj, A. (2004). Understanding the drivers of agricultural land use change in south-central Senegal. Journal of Arid Environments, 59(3), 565–582.

    Article  Google Scholar 

  • Wright, I. A., Smeets, P. J. A. M., Elbersen, B. S., Roos Klein-Lankhorst, J., Pflimlin, A., Louloudis, L., . . . Pirringer, H. (1999). A protocol for building the ELPEN livestock policy decision support system. Aberdeen: Macaulay Land Use Research Institute - MLURI.

    Google Scholar 

  • Wu F. 1998. SimLand: a prototype to simulate land conversion through the integrated GIS and CA with AHP-derived transition rules, International Journal of Geographical Information Science, 12, 1: 63–82.

    Article  Google Scholar 

  • Yeh, A.G.O. and Li, X. (2001) Measurement and Monitoring of Urban Sprawl in a Rapidly Growing Region Using Entropy. Photogrammetric Engineering and Remote Sensing, 67, 83–90.

    Google Scholar 

  • Yeh, A. G.-O., & Li, X. (2002). A Cellular Automata Model to Simulate Development Density for Urban Planning. Environment and Planning B: Planning and Design, 29(3), 431–450. https://doi.org/https://doi.org/10.1068/b1288

    Article  Google Scholar 

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Authors and Affiliations

  1. Azores Regional Statistics Service, Angra do Heroísmo, Portugal

    Paulo Silveira

  2. Azorean Regional Secretary for the Environment and Climate Change, São Jorge, Portugal

    Paulo Silveira

  3. Faculty of Agriculture and Environment, Centre for Applied Economics of the Atlantic, University of the Azores, Angra do Heroísmo, Portugal

    Tomaz Ponce Dentinho

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Silveira, P., Dentinho, T.P. (2024). Land Use Simulation Models. In: Spatial Interaction Models with Land Use. Contributions to Regional Science. Springer, Cham. https://doi.org/10.1007/978-3-031-55008-9_2

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