Mining Multicity Urban Data for Sustainable Population Relocation
In this research, we propose to conduct diagnostic and
predictive analysis about the key factors and consequences of urban
population relocation. To achieve this goal, urban simulation models
extract the urban development trends as land use change patterns from
a variety of data sources. The results are treated as part of urban big
data with other information such as population change and economic
conditions. Multiple data mining methods are deployed on this data to
analyze nonlinear relationships between parameters. The result
determines the driving force of population relocation with respect to
urban sprawl and urban sustainability and their related parameters.
This work sets the stage for developing a comprehensive urban
simulation model for catering to specific questions by targeted users. It
contributes towards achieving sustainability as a whole.
[1] Ewing, R., & Hamidi, S. (2014). Measuring Sprawl 2014. Retrieved from
http://www.smartgrowthamerica.org/documents/measuring-sprawl-2014.
pdf
[2] Smartgrowthamerica.org, 'What is "smart growth?" | Smart Growth
America', 2015. (Online). Available:
http://www.smartgrowthamerica.org/what-is-smart-growth.
[3] Nagy, R., & Lockaby, B. (2010). Urbanization in the Southeastern United
States: Socioeconomic forces and ecological responses along an
urban-rural gradient. Urban Ecosystems, 14(1), 71-86.
doi:10.1007/s11252-010-0143-6
[4] Li, X., & Gar-On Yeh, A. (2004). Data mining of cellular automata's
transition rules. International Journal Of Geographical Information
Science, 18(8), 723-744. doi:10.1080/13658810410001705325
[5] Miller, H., & Han, J. (2001). Geographic data mining and knowledge
discovery. London: Taylor & Francis.
[6] Rajasekar, U., & Weng, Q. (2009). Application of Association Rule
Mining for Exploring the Relationship between Urban Land Surface
Temperature and Biophysical/Social Parameters. Photogrammetric
Engineering & Remote Sensing, 75(4), 385-396.
doi:10.14358/pers.75.4.385
[7] Schneider, A., & Woodcock, C. (2008). Compact, Dispersed,
Fragmented, Extensive? A Comparison of Urban Growth in Twenty-five
Global Cities using Remotely Sensed Data, Pattern Metrics and Census
Information. Urban Studies, 45(3), 659-692.
doi:10.1177/0042098007087340
[8] Göktuğ, M. (2012). Urban Sprawl and Public Policy: A Complexity
Theory Perspective. Emergence: Complexity & Organization, 14(4),
1-16.
[9] Santé, I., García, A., Miranda, D., & Crecente, R. (2010). Cellular
automata models for the simulation of real-world urban processes: A
review and analysis. Landscape And Urban Planning, 96(2), 108-122.
doi:10.1016/j.landurbplan.2010.03.001
[10] Branch, G. (2015). 2010 Urban Area Facts - Geography - U.S. Census
Bureau. Census.gov. Retrieved 18 February 2015,
https://www.census.gov/geo/reference/ua/uafacts.html
[11] Agrawal R., Imieliński T. and Swami A., 'Mining association rules
between sets of items in large databases', ACM SIGMOD Record, vol. 22,
no. 2, pp. 207-216, 1993.
[12] MacQueen, J. Some methods for classification and analysis of
multivariate observations. Proceedings of the Fifth Berkeley Symposium
on Mathematical Statistics and Probability, Volume 1: Statistics,
281—297.
[13] Quinlan J.R., C4.5. San Mateo, Calif.: Morgan Kaufmann Publishers,
1993.
[14] Hamidi, S., & Ewing, R. (2014). A longitudinal study of changes in urban
sprawl between 2000 and 2010 in the United States. Landscape And
Urban Planning, 128, 72-82. doi:10.1016/j.landurbplan.2014.04.021
[15] Gis.cancer.gov,. (2015). County Level Urban Sprawl Indices -
Geographic Information Systems & Science. Retrieved 25 April 2015,
from http://gis.cancer.gov/tools/urban-sprawl/
[16] Pampoore-Thampi, A. & Varde, A. (2014). Mining GIS Data to Predict
Urban Sprawl, ACM conference on Knowledge Discovery and Data
Mining (KDD Bloomberg Track), New York City, NY, pp 118-125.
[17] IEEE Smart Cities, http://smartcities.ieee.org/
[18] Vienna University of Technology et al., European Smart Cities,
www.smart-cities.eu
[19] Smartcitiescouncil.com, "Smart Cities Council | Definitions and
overviews", 2015. (Online). Available:
http://smartcitiescouncil.com/smart-cities-information-center/definitions
-and-overviews.
[20] Pawlish, M., Varde, A., Robila, S. and Ranganathan, A. (2014). A Call for
Energy Efficiency in Data Centers, Journal of ACM’s Special Interest
Group on Management of Data Record (SIGMOD Record), 2014, Vol.
43, No. 1, pp. 45-51.
[21] Varde A., and Du X., Multicity Simulation with Data Mining for Urban
Sustainability, Presentation at Bloomberg Data Science Labs, March
2015.
[1] Ewing, R., & Hamidi, S. (2014). Measuring Sprawl 2014. Retrieved from
http://www.smartgrowthamerica.org/documents/measuring-sprawl-2014.
pdf
[2] Smartgrowthamerica.org, 'What is "smart growth?" | Smart Growth
America', 2015. (Online). Available:
http://www.smartgrowthamerica.org/what-is-smart-growth.
[3] Nagy, R., & Lockaby, B. (2010). Urbanization in the Southeastern United
States: Socioeconomic forces and ecological responses along an
urban-rural gradient. Urban Ecosystems, 14(1), 71-86.
doi:10.1007/s11252-010-0143-6
[4] Li, X., & Gar-On Yeh, A. (2004). Data mining of cellular automata's
transition rules. International Journal Of Geographical Information
Science, 18(8), 723-744. doi:10.1080/13658810410001705325
[5] Miller, H., & Han, J. (2001). Geographic data mining and knowledge
discovery. London: Taylor & Francis.
[6] Rajasekar, U., & Weng, Q. (2009). Application of Association Rule
Mining for Exploring the Relationship between Urban Land Surface
Temperature and Biophysical/Social Parameters. Photogrammetric
Engineering & Remote Sensing, 75(4), 385-396.
doi:10.14358/pers.75.4.385
[7] Schneider, A., & Woodcock, C. (2008). Compact, Dispersed,
Fragmented, Extensive? A Comparison of Urban Growth in Twenty-five
Global Cities using Remotely Sensed Data, Pattern Metrics and Census
Information. Urban Studies, 45(3), 659-692.
doi:10.1177/0042098007087340
[8] Göktuğ, M. (2012). Urban Sprawl and Public Policy: A Complexity
Theory Perspective. Emergence: Complexity & Organization, 14(4),
1-16.
[9] Santé, I., García, A., Miranda, D., & Crecente, R. (2010). Cellular
automata models for the simulation of real-world urban processes: A
review and analysis. Landscape And Urban Planning, 96(2), 108-122.
doi:10.1016/j.landurbplan.2010.03.001
[10] Branch, G. (2015). 2010 Urban Area Facts - Geography - U.S. Census
Bureau. Census.gov. Retrieved 18 February 2015,
https://www.census.gov/geo/reference/ua/uafacts.html
[11] Agrawal R., Imieliński T. and Swami A., 'Mining association rules
between sets of items in large databases', ACM SIGMOD Record, vol. 22,
no. 2, pp. 207-216, 1993.
[12] MacQueen, J. Some methods for classification and analysis of
multivariate observations. Proceedings of the Fifth Berkeley Symposium
on Mathematical Statistics and Probability, Volume 1: Statistics,
281—297.
[13] Quinlan J.R., C4.5. San Mateo, Calif.: Morgan Kaufmann Publishers,
1993.
[14] Hamidi, S., & Ewing, R. (2014). A longitudinal study of changes in urban
sprawl between 2000 and 2010 in the United States. Landscape And
Urban Planning, 128, 72-82. doi:10.1016/j.landurbplan.2014.04.021
[15] Gis.cancer.gov,. (2015). County Level Urban Sprawl Indices -
Geographic Information Systems & Science. Retrieved 25 April 2015,
from http://gis.cancer.gov/tools/urban-sprawl/
[16] Pampoore-Thampi, A. & Varde, A. (2014). Mining GIS Data to Predict
Urban Sprawl, ACM conference on Knowledge Discovery and Data
Mining (KDD Bloomberg Track), New York City, NY, pp 118-125.
[17] IEEE Smart Cities, http://smartcities.ieee.org/
[18] Vienna University of Technology et al., European Smart Cities,
www.smart-cities.eu
[19] Smartcitiescouncil.com, "Smart Cities Council | Definitions and
overviews", 2015. (Online). Available:
http://smartcitiescouncil.com/smart-cities-information-center/definitions
-and-overviews.
[20] Pawlish, M., Varde, A., Robila, S. and Ranganathan, A. (2014). A Call for
Energy Efficiency in Data Centers, Journal of ACM’s Special Interest
Group on Management of Data Record (SIGMOD Record), 2014, Vol.
43, No. 1, pp. 45-51.
[21] Varde A., and Du X., Multicity Simulation with Data Mining for Urban
Sustainability, Presentation at Bloomberg Data Science Labs, March
2015.
@article{"International Journal of Information, Control and Computer Sciences:71778", author = "Xu Du and Aparna S. Varde", title = "Mining Multicity Urban Data for Sustainable Population Relocation", abstract = "In this research, we propose to conduct diagnostic and
predictive analysis about the key factors and consequences of urban
population relocation. To achieve this goal, urban simulation models
extract the urban development trends as land use change patterns from
a variety of data sources. The results are treated as part of urban big
data with other information such as population change and economic
conditions. Multiple data mining methods are deployed on this data to
analyze nonlinear relationships between parameters. The result
determines the driving force of population relocation with respect to
urban sprawl and urban sustainability and their related parameters.
This work sets the stage for developing a comprehensive urban
simulation model for catering to specific questions by targeted users. It
contributes towards achieving sustainability as a whole.", keywords = "Data Mining, Environmental Modeling,
Sustainability, Urban Planning.", volume = "9", number = "12", pages = "2530-8", }
