Web Intelligence and Data Mining in Urban Areas

  • Anandakumar Haldorai
  • Arulmurugan Ramu
  • Suriya Murugan
Part of the Urban Computing book series (UC)


The development of urbanization and improvement of intelligent urban areas need better procedures for planning of urban zones. In urban communities with old structures, residents invest an excess of energy doing dreary and futile exercises like holding up in lines, heading out long separations to purchase products or get benefits, and being stuck in roads turned parking lots. There are different issues consider as air contamination, ecological issues, old structures, nonstandard urban foundations, and media transmission frameworks. To adapt to present circumstances, a city needs savvy frameworks and parts including a keen economy, shrewd transportation, brilliant condition, brilliant natives, shrewd way of life, and organization. To plan such frameworks and parts in a savvy city, there ought to be an instrument which can process the put away information and give the resultant data to the administration and clients. In such manner, information mining and Web insight are compelling devices which have a noteworthy job in structuring a shrewd city and preparing huge information. At that point keen parts, the foundations of a smart city, and the job of information mining in building up an urban city are examined subsequent to displaying ideas and definitions.


Web intelligence Data mining Intelligence gathering and analysis Digital analytics 


  1. 1.
    Kleinberg, M.: Authoritative sources in a hyperlinked environment. J. ACM. 46(5), 604–632 (1999)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Golbeck, J., Rothstein, M.: Linking social networks on the web with FOAF: a semantic web case study. In: AAAI 2008: Proc. of the 23rd National Conference on Artificial Intelligence, pp. 1138–1143. AAAI Press, Menlo Park (2008)Google Scholar
  3. 3.
    Akerkar, R., Aaberge, T.: Semantically linking virtual communities. In: El Morr, C., Maret, P. (eds.) Virtual Community Building and the Information Society: Current and Future Directions, pp. 192–207. IGI Global Publishers, Hershey (2011)Google Scholar
  4. 4.
    Shadbolt, N., Berners-Lee, T., Hall, W.: The semantic web revisited. IEEE Intell. Syst. 21(3), 96–101 (2006)CrossRefGoogle Scholar
  5. 5.
    Conallen, J.: Building Web Applications with UML, 2nd edn. Addison-Wesley, Boston (2003)Google Scholar
  6. 6.
    Hassan, A.: Architecture recovery of web applications. Master’s thesis, University of Waterloo (2001)Google Scholar
  7. 7.
    Deshpande, Y., Murugesan, S., Ginige, A., Hansen, S., Schwabe, D., Gaedke, M., White, B.: Web engineering. J. Web Eng. 1(1), 3–17 (2002)Google Scholar
  8. 8.
    Norton, K.: Applying cross-functional evolutionary methodologies to web development. Web Eng. 2016, 48–57 (2001)CrossRefGoogle Scholar
  9. 9.
    Jiawei, H., Chang, K.: Data mining for web intelligence. Computer. 35(11), 64–70 (2002)CrossRefGoogle Scholar
  10. 10.
    Rogan, J., Chen, D.: Remote sensing technology for mapping and monitoring land-cover and land-use change. Prog. Plan. 61, 301–325 (2004)CrossRefGoogle Scholar
  11. 11.
    Chan, W., Chan, P., Yeh, O.: Detecting the nature of change in an urban environment: a comparison of machine learning algorithms. Photogramm. Eng. Remote. Sens. 67, 213–225 (2001)Google Scholar
  12. 12.
    Seto, C., Kaufmann, K.: Modeling the drivers of urban land use change in the Pearl River Delta, China: integrating remote sensing with socioeconomic data. Land Econ. 79, 106–121 (2003)CrossRefGoogle Scholar
  13. 13.
    Friedman, Z., Angelici, G.: The detection of urban expansion from Landsat imagery. Remote Sens. Q. 1, 58–79 (1979)Google Scholar
  14. 14.
    Michalak, Z.: GIS in land use change analysis — integration of remotely sensed data into GIS. Appl. Geogr. 13, 28–44 (1993)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Romero, H., Ihl, M., Rivera, A., Zalazar, P., Azocar, P.: Rapid urban growth, land-use changes and air pollution in Santiago, Chile. Atmos. Environ. 33, 4039–4047 (1999)CrossRefGoogle Scholar
  16. 16.
    Carlson, N., Arthur, T.: The impact of land use — land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Glob. Planet. Chang. 25, 49–65 (2000)CrossRefGoogle Scholar
  17. 17.
    Robinson, L., Newell, P., Marzluff, A.: Twenty-five years of sprawl in the Seattle region: growth management responses and implications for conservation. Landsc. Urban Plan. 71, 51–72 (2005)CrossRefGoogle Scholar
  18. 18.
    Tatem, J., Hay, I.: Measuring urbanization pattern and extent for malaria research: a review of remote sensing approaches. J. Urban Health. 81, 363–376 (2004)CrossRefGoogle Scholar
  19. 19.
    Mills, G.: Cities as agents of global change. Int. J. Climatol. 27, 1849–1857 (2007)CrossRefGoogle Scholar
  20. 20.
    Pataki, E., Alig, J., Fung, S., Golubiewski, E., Kennedy, A., McPherson, G., Nowak, J., Pouyat, V., Romero Lankao, P.: Urban ecosystems and the North American carbon cycle. Glob. Chang. Biol. 12, 2092–2102 (2006)CrossRefGoogle Scholar
  21. 21.
    Schneider, A., Friedl, A., Potere, D.: A new map of global urban extent from MODIS satellite data. Environ. Res. Lett. 4, 044003 (2009)CrossRefGoogle Scholar
  22. 22.
    Schneider, A., Friedl, A., Potere, D.: Mapping urban areas globally using MODIS 500m data: new methods and datasets based on urban ecoregions. Remote Sens. Environ. 114, 1733–1746 (2010)CrossRefGoogle Scholar
  23. 23.
    Ehlers, M., Jadkowski, A., Howard, R., Brostuen, E.: Application of SPOT data for regional growth analysis and local planning. Photogramm. Eng. Remote. Sens. 56, 175–180 (1990)Google Scholar
  24. 24.
    Jensen, R., Toll, L.: Detecting residential land use development at the urban fringe. Photogramm. Eng. Remote. Sens. 48, 629–643 (1982)Google Scholar
  25. 25.
    Ulbricht, A., Heckendorff, D.: Satellite images for recognition of landscape and land use changes. ISPRS J. Photogramm. Remote Sens. 53, 235–243 (1998)CrossRefGoogle Scholar
  26. 26.
    Yang, X., Lo, P.: Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. Int. J. Remote Sens. 23, 1775–1798 (2002)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Ehrlich, I.: On the relation between education and crime. In: Education, Income, and Human Behavior, pp. 313–338. NBER, Cambridge (1975)Google Scholar
  28. 28.
    Kennedy, B., Kawachi, I., Prothrow-Stith, D., Lochner, K., Gupta, V.: Social capital, income inequality, and firearm violent crime. Soc. Sci. Med. 47(1), 7–17 (1998)CrossRefGoogle Scholar
  29. 29.
    Patterson, E.: Poverty, income inequality, and community crime rates. Criminology. 29(4), 755–776 (1991)CrossRefGoogle Scholar
  30. 30.
    Braithwaite, J.: Crime, Shame and Reintegration. Cambridge University Press, Cambridge (1989)CrossRefGoogle Scholar
  31. 31.
    Wang, H., Kifer, D., Graif, C., Li, Z.: Crime rate inference with big data. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 635–644. ACM, New York (2016)CrossRefGoogle Scholar
  32. 32.
    Wang, X., Brown, D., Gerber, M.: Spatiotemporal modeling of criminal incidents using geographic, demographic, and twitter-derived information. In: 2012 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 36–41. IEEE, Piscataway (2012)CrossRefGoogle Scholar
  33. 33.
    Wang, X., Gerber, M., Brown, D.: Automatic crime prediction using events extracted from twitter posts. In: International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, pp. 231–238. Springer, New York (2012)CrossRefGoogle Scholar
  34. 34.
    de Queiroz Neto, J., dos Santos, E., Vidal, C.: MSKDE-using marching squares to quickly make high quality crime hotspot maps. In: 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 305–312. IEEE, Piscataway (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSri Eshwar College of EngineeringCoimbatoreIndia
  2. 2.Department of Computer Science and EngineeringPresidency UniversityYelahanka, BengaluruIndia
  3. 3.Department of Computer Science and EngineeringKPR Institute of Engineering and TechnologyCoimbatoreIndia

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