A model for zoning of urban areas using AZP algorithm

Document Type : Research Paper


1 Associate Professor, Faculty of Geography, University of Tehran, Iran

2 MA Student in Remote Sensing and Geographic Information System, University of Tehran, Iran

3 MA Student in Urban Planning, Department of Art and Architecture, Islamic Azad University, Science and Research Branch, Iran

4 Associate Professor, Urban Planning, Faculty of Geography, University of Zanjan, Iran


Urban planners require a deep understanding of city and urban planning for detection and resolving of urban problems. Because cities are composed of a wide variety of societies and infinite number of dimensions, urban planners couldn’t devise and apply a similar plan for whole city. Thus, the city must be divided into spatial units based on social, economic and physical aspects. This segmentation is used for some constraints like specific land use or construction density to control urban development. This kind of segmentation can be defined through lots of urban plans such as detailed plans and comprehensive plan. On the other hand, in city there are lots of organizations like municipalities, roads and urban development organizations, Tavanir companies, Abfa companies and etc. with their own segmentations for servicing their clients. All of these configurations have been made to respond specific requirements and achieve some goals. However, most of the times these segmentations have functional overlap and in many cases conflict with each other. They aren’t, sometimes, efficient enough to do their functions properly. They usually make people unsatisfied because they oblige to explore lots of organizations to do their work. On the other hand, the number of units and divisions of all municipals, organizations and agencies are not fixed. It has been increased through the time due to the increasing population and urban expansion. Such a situation with inconsistent spatial structure and lack of public participation, are some reasons which make it difficult for urban sustainable management. Therefore, the aim of this article is to apply new approaches and methodologies to solve these problems. The AZP is one of the many different methods to do that. AZP is an algorithm used extensively for delimitation of multidimensional units or detection of spatial scale to study specific relationships in space of a city. In this article, we want to apply this model in urban areas of Iran. Zanjan city is case study of this research.
Spatial clustering methods (such as AZP) have been developed over time. They could be used for making zonation by aggregation and interchange of basic spatial units in each other with optimization of objective functions. These regionalization algorithms have some basic features. All of them integrate basic spatial units into predefined number of regions with optimization of an aggregation function. The basic spatial units assigned to a region must be spatially connected and the maximum number of regions should be one less than basic spatial units. A basic spatial unit could be aggregated to only one region and minimum number of basic spatial units should be assigned to a region. They have a supervise capability. Thus, relevant variables can define number of regions and types of objective function. The AZP algorithm can work with any type of objective function that is sensitive to the aggregation of data for N basic spatial units into M regions; for example functions extracted directly from the data (for instance sum of squared deviations from average zone size) or functions that can represent the goodness of a fit of a model applied to the data (fit of a linear regression model or the performance of a spatial interaction model). Output regions will not change over time and can make urban management more sustainable and more coordinated. These configurations could be used as basic directorial units of different organizations. Type of this research is applied and the purpose is development of unified multidimensional regionalization. This can assist sustainable management of city. We used census block and land use types of Zanjan city as basic data. Then, we extract 26 indicators from those data and aggregate them into fishnet with cell size of 300 meter. In the next step, to decrease the heterogeneity and discovering general trends, we applied Principal Component Analysis (PCA) on the indicators. Therefore, five PCs are extracted which control 76 percent of variance. Regionalization has been conducted based on these PSs. Objective functions are an intra-area correlation and a shape function to optimize output regions. The homogeneity of the regions can be evaluated based on a direct measure of within-area homogeneity, the intra-area correlation (IAC). We measured shape compactness by comprising the squared perimeter. Finally, after running the algorithm and exporting results, we need to test our output. Moran's I statistic is a measure of spatial autocorrelation in which Negative (positive) values indicate negative (positive) spatial autocorrelation. The values range from −1 (indicating perfect dispersion) to +1 (perfect correlation) and a zero value indicates a random spatial pattern.
Results and Discussion
We used 13 factors for validation of our work. Resulted regionalization has been compared with regionalization of detailed plan of Zanjan city for validation of AZP algorithm based on Moran's I statistics. The Moran's I showed detailed plan regionalization which have clustered spatial pattern. AZP algorithm could create regions with random spatial pattern which indicate homogenous regionalization.
This research showed that unknown pattern should be recognized by new methodologies so urban planners could do planning more efficiently. We should not limit our research to earlier constructed regions because every regionalization have been made for specific purpose and certainly would affect the results of the analysis. Therefore, at first step there is a need to make our objective-related regions. These regions, then, could be statistically used in the meaning of spatial units which are free from MAUP and very reliable.
This research showed that unknown pattern should be recognized by new methodologies for more efficiently planning by urban planners. We should not limit our research to earlier constructed regions because every regionalization have been made for specific purpose and certainly would affect the result of analysis. Thus, at first step, there is a need to make our objective-related regions.


Main Subjects

  1. هادی­پور، حلیمه­خاتون، فرهودی، رحمت­اله و احمد پوراحمد،، معیارهای مؤثر در مرزبندی نواحی شهری (مورد مطالعه: منطقۀ یک شهرداری تهران)، پژوهش‌های جغرافیایی، سال 1385، شمارۀ 56، صص 93- 111.

    حبیبی، کیومرث، پوراحمد، احمد و ابوالفضل مشکینی، 1387، از زنگان تا زنجان (سیری تاریخی بر تحولات کالبدی - فضایی بافت کهن شهر)، دانشگاه زنجان، زنجان.

    کلانتری، خلیل، 1393، مدل‌های کمی در برنامه‌ریزی (منطقه‌ای، شهری و روستایی)، فرهنگ صبا، تهران.

    مرادی، داوود، 1391، جزوة تحلیل نواحی اجتماعی، دانشگاه هنر اصفهان، اصفهان.

    زیاری، کرامت‌اله، 1388، برنامه‌ریزی کاربری اراضی شهری، انتشارات دانشگاه تهران، تهران.

    ضرابی، اصغر و رحمتی، صفر قائد، تحلیل پیامدهای ناشی از تنوع منطقه‌بندی درون‌شهری (نمونة موردی: منطقه‌بندی شهر اصفهان)، مجلة پژوهشی دانشگاه اصفهان، سال 1386، شمارة 6، صص 33- 43.

    ویچرن، دین دبلیو و ریچارد آرنولد جانسون، 1392، تحلیل آماری چندمتغیری کاربردی، ترجمۀ حسینعلی نیرومند، دانشگاه فردوسی مشهد، مشهد.

    فرهودی، رحمت‌اله، قالیباف، محمدباقر، چهارراهی، ذبیح‌اله، جواهری، احمد ،. تحلیل تقسیمات کالبدی شهری براساس مدیریت یکپارچۀ شهری (نمونۀ موردی: شهر شیراز)، نشریۀ جغرافیا، سال 1388، شمارۀ 19 و 18، صص 27- 44.


    1. Amrhein, C., Searching For the Elusive Aggregation Effect: Evidence from Statistical Simulations, Environment and Planning- Part A, 1955 27, PP. 105-119.
    2. Arbia, G., 1989, Spatial Data Configuration in Statistical Analysis of Regional Economic and Related Problems, Kluwer Academic, Dordrecht.
    3. Bernabe, L., Duque, C., Ramirez, R. and Osorio, L., 2008, Classification over Geographical Zones: A Combinatorial Optimization Approach to the Regional Partitioning Problem, Electronics, Communications and Computers, 18th International Conference on IEEE, PP. 70-74.
    4. Christina, J. and Komathy, K., 2013, Analysis of Hard Clustering Algorithms Applicable to Regionalization, Information and Communication Technologies (ICT), 2013 IEEE Conference on IEEE, PP. 606-610.
    5. Cockings, S., Harfoot, A., Martin, D. and Hornby, D., Maintaining Existing Zoning Systems Using Automated Zone-Design Techniques: Methods for Creating the 2011 Census Output Geographies for England and Wales, Environment and Planning-Part A, 2011 43, 2399.
    6. Cullingworth, J. B. 1997. British Land-Use Planning: A Failure to Cope with Change, Urban Studies, Vol. 34, No. 5-6, PP. 945-960.
    7. Datta, D., Malczewski, J. and Figueira, J. R., Spatial Aggregation and Compactness of Census Areas with a Multiobjective Genetic Algorithm: A Case Study In Canada, Environment and Planning-Part B, 2012 39, P. 376.
    8. Drackley, A., Newbold, K. B. and Taylor, C., 2011, Defining Socially-Based Spatial Boundaries in The Region of Peel, Ontario, Canada, International Journal of Health Geographics 10, 38.
    9. Duque, J. C., Anselin, L. and Rey, S. J., THE MAXPREGIONS PROBLEM*, Journal of Regional Science2012 52, PP. 397-419.
    10. Duque, J. C., Artís, M. and Ramos, R., The Ecological Fallacy in a Time Series Context: Evidence From Spanish Regional Unemployment Rates, Journal of Geographical Systems, 2006 8, PP. 391-410.
    11. Duque, J. C., Church, R. L. and Middleton, R. S., The PRegions Problem, Geographical Analysis, 2011 43, PP. 104-126.
    12. Duque, J. C., Ramos, R. and Suriñach, J., Supervised Regionalization Methods: A Survey, International Regional Science Review, 2007 30, PP. 195-220.
    13. Duque, J. C., Royuela, V. and Noreña, M., A Stepwise Procedure to Determinate a Suitable Scale for the Spatial Delimitation of Urban Slums, Defining the Spatial Scale in Modern Regional Analysis, Springer 2012, PP. 237-254.
    14. Evans, A. W., 2003. Shouting Very Loudly: Economics, Planning and Politics, Town Planning Review, Vol. 74, No. 2, PP. 195-212.
    15. Farhoodi, R., Ghalibaf, M. B., Chaharrahi, Z., Javaheri, A., Analysis of Urban Physical Divisions Based on Sustainable Management (Case Study: Shiraz City), Geography (Iranian Geographic Association Publication), Vol. 2009, No. 18-19, PP. 27-44. (In Persian)
    16. Grady, S. and Enander, H., Geographic Analysis of Low Birth-weight and Infant Mortality in Michigan Using Automated Zoning Methodology, International Journal of Health Geographics, Vol. 8, No. 10, PP. 2009.
    17. Guo, D. and Wang, H., Automatic Region Building for Spatial Analysis, Transactions in GIS 2011 15, PP. 29-45.
    18. Habibi, K., Poorahmad, A. and Meshkini, A., 2010, From Zangan to Zanjan (A Review of Physical-Spatial Transformation of Old Town Texture), Zanjan University Press, Zanjan. (In Persian)
    19. Hadipoor, H., Farhoodi, R. and Poorahmad, A., Effective Criterions in Delimitation of Urban Areas (Case Study: 1st District of Tehran Municipality), Journal of Geographical Researches, Vol. 2006, No. 56, PP. 93-111. (In Persian)
    20. Heye, C. and Leuthold, H., 2005, Theory-Based Social Area Analysis: An Approach Considering The Conditions of a Post-Industrial Society', 14th European Colloquium on Theoretical and Quantitative Geography, PP. 9-13.
    21. Jiménez, A.V. and Morollón, F. R., 2011, An Analytical Regions Proposal for the Study of Labour Markets: An Evaluation for the Spanish Territory, Documentos de Trabajo FUNCAS, 1.
    22. Vechern, D. W. and Janson, R. A., 2011, Applied Multivariate Statistical Analysis, Translated by: Niroomand, H., Ferdowsi University Press, Mashhad. (In Persian)
    23. Kalantari, K., 2012, Quantitative Models in Planning (Regional, Urban and Rural), Farhang-e- Saba Publications, Tehran. (In Persian)
    24. Kim, H., Chun, Y. and Kim, K., 2013, Delimitation of Functional Regions Using a P-Regions Problem Approach, International Regional Science Review.
    25. Jones, C., Leishman, C. and Watkins, C., 2005, Housing Market Processes, Urban Housing Submarkets and Planning Policy, Town Planning Review, Vol. 76, No. 2, PP. 215-233.
    26. Li, W., Goodchild, M. F. and Church, R., 2013, An Efficient Measure of Compactness for Two-Dimensional Shapes and its Application in Regionalization Problems, International Journal of Geographical Information Science, PP. 1-24.
    27. Mennis, J. and Guo, D., Spatial Data Mining and Geographic Knowledge Discovery- An Introduction, Computers, Environment and Urban Systems, 2009 33, PP. 403-408.
    28. Mimis, A., Rovolis, A. and Stamou, M., An AZP-ACO Method for Region-Building, Artificial Intelligence: Theories and Applications, Springer 2012, PP. 81-89.
    29. Moradi, D., 2012, Analysis of Social Areas Booklet, Isfahan Art University, Isfahan. (In Persian)
    30. Openshaw, S., A Geographical Solution to Scale and Aggregation Problems in Region-Building, Partitioning and Spatial Modeling, Transactions of the Institute of British Geographers, Vol. 09, No. 1977a, PP. 459-472.
    31. Openshaw, S., Optimal Zoning Systems for Spatial Interaction Models, Environment and Planning- A, 1977b 9, PP. 169-184.
    32. Openshaw, S. and Baxter, R., Algorithm 3: A Procedure to Generate Pseudo-Random Aggregations of N Zones Into M Zones, Where M is Less Than N, Environment and Planning- A, 1977 9, PP. 1423-1428.
    33. Openshaw, S. and Rao, L., Algorithms for Reengineering 1991 Census Geography, Environment and planning- A, 1955 27, PP. 425-446.
    34. O'Sullivan, T. and Scotland, C., 2004, Local Housing System Analysis: Good Practice Guide, Communities Scotland Edinburgh.
    35. Ralphs, M., Ang, L. and Zealand, S. N., 2009, Optimized Geographies for Data Reporting: Zone Design Tools for Census Output Geographies, Statistics New Zealand.
    36. Romano, E., Balzanella, A. and Verde, R., 2010, A New Regionalization Method for Spatially Dependent Functional Data Based on Local Variogram Models: An Application on Environmental Data, Atti del 45th Scientific Meeting of the Italian Statistical Society, Padova.
    37. Royuela, V. and Duque, J. C., 2012, HouSI: Heuristic for Delimitation of Housing Submarkets and Price Homogeneous Areas, Computers, Environment and Urban Systems.
    38. Royuela, V., Romaní, J. and Artís, M., 2009, Using Quality of Life Criteria to Define Urban Areas in Catalonia, Social Indicators Research, No 90, PP. 419-440.
    39. Sabel, C. E., Kihal, W., Bard, D. and Weber, C., 2012, Creation of Synthetic Homogeneous Neighbourhoods Using Zone Design Algorithms to Explore Relationships Between Asthma and Deprivation in Strasbourg, France, Soc. Sci. Med.
    40. Santos, S. M., Chor, D. and Werneck, G. L., Demarcation of Local Neighborhoods to Study Relations Between Contextual Factors and Health, International Journal of Health Geographics, 2010 9, PP. 34.
    41. Wang, F., Guo, D. and McLafferty, S., Constructing Geographic Areas for Cancer Data Analysis: A Case Study on Late-Stage Breast Cancer Risk in Illinois, Applied Geography, 2012 35, PP. 1-11.
    42. 27.        Zarabi, A. and Rahmati, S., Analysis of Different Intra-Urban Regionalization Outcomes (Case Study: Isfahan City Regionalization), Isfahan Research Journal, Vol. 2007, No. 6, PP. 33-42. (In Persian)
    43. Ziyari, K., 2009, Urban Land Use Planning, University Of Tehran Press, Tehran. (In Persian)


Volume 47, Issue 4 - Serial Number 4
January 2016
Pages 689-707
  • Receive Date: 18 October 2013
  • Revise Date: 05 September 2014
  • Accept Date: 07 September 2014
  • First Publish Date: 22 December 2015