Data mining techniques to analyze the factors influencing active commuting to school

References

• Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD international conference on management of data (pp. 207–216).
   Google Scholar
• Babey, S. H., Hastert, T. A., Huang, W., & Brown, E. R. (2009). Sociodemographic, family, and environmental factors associated with active commuting to school among US adolescents. Journal of Public Health Policy, 30, 203–220.
   PubMed Web of Science ®Google Scholar
• Borgelt, C. (2003). Efficient implementations of Apriori and Eclat. Workshop of Frequent Item Set Mining Implementations (FIMI 2003, Melbourne, FL, USA).
   Google Scholar
• Borgelt, C., & Kruse, R. (2002). Induction of association rules: Apriori implementation. In 15th Conference on Computational Statistics (COMPSTAT 2002, Berlin, Germany). Heidelberg, Germany: Physica Verlag.
   Google Scholar
• Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Boca Raton, FL: CRC Press.
   Google Scholar
• Bringolf-Isler, B., Grize, L., Mader, U., Ruch, N., Sennhauser, F. H., Braun-Fahrlander, C., & Team, S. (2008). Personal and environmental factors associated with active commuting to school in Switzerland. Preventive Medicine, 46(1), 67–73.
   PubMed Web of Science ®Google Scholar
• Carver, A., Timperio, A., & Crawford, D. (2008). Playing it safe: The influence of neighbourhood safety on children’s physical activity—A review. Health Place, 14(2), 217–227. doi: 10.1016/j.healthplace.2007.06.004
   PubMed Web of Science ®Google Scholar
• Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16–28.
   Web of Science ®Google Scholar
• Chillón, P., Hales, D., Vaughn, A., Gizlice, Z., Ni, A., & Ward, D. S. (2014). A cross-sectional study of demographic, environmental and parental barriers to active school travel among children in the United States. International Journal of Behavioral Nutrition and Physical Activity, 11(1), 61.
   PubMed Web of Science ®Google Scholar
• Chillón, P., Herrador-Colmenero, M., Migueles, J. H., Cabanas-Sánchez, V., Fernández-Santos, J. R., Veiga, Ó. L., … Gómez-Gallego, F. (2017). Convergent validation of a questionnaire to assess the mode and frequency of commuting to and from school. Scandinavian Journal of Public Health, 45(6), 612–620.
   PubMed Web of Science ®Google Scholar
• Chillón, P., Ortega, F. B., Ruiz, J. R., De Bourdeaudhuij, I., Martínez-Gómez, D., Vicente-Rodriguez, G., … Sjöström, M. (2011). Active commuting and physical activity in adolescents from Europe: results from the HELENA study. Pediatric Exercise Science, 23(2), 207.
   PubMed Web of Science ®Google Scholar
• Chillón, P., Ortega, F. B., Ruiz, J. R., Evenson, K. R., Labayen, I., Martinez-Vizcaino, V., … Sjöström, M. (2012). Bicycling to school is associated with improvements in physical fitness over a 6-year follow-up period in Swedish children. Preventive Medicine, 55(2), 108–112.
   PubMed Web of Science ®Google Scholar
• Chillón, P., Panter, J., Corder, K., Jones, A. P., & Van Sluijs, E. M. (2015). A longitudinal study of the distance that young people walk to school. Health & Place, 31, 133–137.
   PubMed Web of Science ®Google Scholar
• Chizi, B., & Maimon, O. (2005). Dimension reduction and feature selection. In O. Maimon & L. Rokach (Eds.), Data mining and knowledge discovery handbook (pp. 83–100). New York: Springer.
   Google Scholar
• Cooper, A. R., Wedderkopp, N., Wang, H., Andersen, L. B., Froberg, K., & Page, A. S. (2006). Active travel to school and cardiovascular fitness in Danish children and adolescents. Medicine and Science in Sports and Exercise, 38(10), 1724–1731.
   PubMed Web of Science ®Google Scholar
• Dash, M., & Liu, H. (1997). Feature selection for classification. Intelligent Data Analysis, 1(3), 131–156.
   Google Scholar
• Davison, K. K., Werder, J. L., & Lawson, C. T. (2008). Children's active commuting to school: Current knowledge and future directions. Preventing Chronic Disease, 5(3), A100.
   PubMedGoogle Scholar
• De Geus, B., De Bourdeaudhuij, I., Jannes, C., & Meeusen, R. (2007). Psychosocial and environmental factors associated with cycling for transport among a working population. Health Education Research, 23(4), 697–708.
   PubMed Web of Science ®Google Scholar
• D'Haese, S., De Meester, F., De Bourdeaudhuij, I., Deforche, B., & Cardon, G. (2011). Criterion distances and environmental correlates of active commuting to school in children. International Journal of Behavioral Nutrition and Physical Activity, 8(1), 88.
   PubMed Web of Science ®Google Scholar
• Duncan, J. S., Hopkins, W. G., Schofield, G., & Duncan, E. K. (2008). Effects of weather on pedometer-determined physical activity in children. Medicine and Science in Sports and Exercise, 40(8), 1432–1438.
   PubMed Web of Science ®Google Scholar
• Efroymson, M. A. (1960). Multiple regression analysis. In A. Ralston & H. S. Wilf (Eds.), Mathematical methods for digital computers. New York: Wiley.
   Google Scholar
• Ellis, K., Godbole, S., Marshall, S., Lanckriet, G., Staudenmayer, J., & Kerr, J. (2014). Identifying active travel behaviors in challenging environments using GPS, accelerometers, and machine learning algorithms. Frontiers in Public Health, 2, 39–46.
   PubMedGoogle Scholar
• Ermagun, A., Rashidi, T. H., & Lari, Z. A. (2015). Long-term planning and the impact of modal specification on policy assessment for school trip mode choice behavior. Transportation Research Board 94th Annual Meeting (No. 15-5947).
   Google Scholar
• Farrar, D. E., & Glauber, R. E. (1967). Multicollinearity in regression analysis: The problem revisited. The Review of Economics and Statistics, 49(1), 92–107.
   Web of Science ®Google Scholar
• Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). Knowledge discovery and data mining: Towards a unifying framework. KDD, 96, 82–88.
   Google Scholar
• García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining. New York: Springer.
   Google Scholar
• Garg, A., & Tai, K. (2013). Comparison of statistical and machine learning methods in modelling of data with multicollinearity. International Journal of Modelling, Identification and Control, 18(4), 295–312.
   Google Scholar
• Giles-Corti, B., Foster, S., Shilton, T., & Falconer, R. (2010). The co-benefits for health of investing in active transportation. New South Wales Public Health Bulletin, 21(6), 122–127. doi: 10.1071/NB10027
   PubMedGoogle Scholar
• Gower, J. C. (1971). A general coefficient of similarity and some of its properties. Biometrics, 27(4), 857–871.
   Web of Science ®Google Scholar
• Haapala, E., Poikkeus, A. M., Kukkonen-Harjula, K., Tompuri, T., Lintu, N., Väistö, J., … Lakka, T. (2014). Associations of physical activity and sedentary behavior with academic skills—A follow-up study among primary school children. PLoS One, 9(9), e107031. doi: 10.1371/journal.pone.0107031
   PubMed Web of Science ®Google Scholar
• Hamer, M., & Chida, Y. (2008). Active commuting and cardiovascular risk: A meta-analytic review. Preventive Medicine, 46(1), 9–13.
   PubMed Web of Science ®Google Scholar
• Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
   Web of Science ®Google Scholar
• Hand, D. J. (1998). Data mining: Statistics and more? The American Statistician, 52(2), 112–118.
   Web of Science ®Google Scholar
• Heinen, E., Maat, K., & Van Wee, B. (2011). Day-to-day choice to commute or not by bicycle. Transportation Research Record: Journal of the Transportation Research Board, 2230(1), 9–18.
   Google Scholar
• Hunt, E. B., Marin, J., & Stone, P. J. (1966). Experiments in induction. New York: Academic Press.
   Google Scholar
• Jolliffe, I. (2002). Principal component analysis (2nd ed.). New York: Springer.
   Google Scholar
• Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. Hoboken, NJ: Wiley. doi: 10.1002/9780470316801.ch2
   Google Scholar
• Kingsford, C., & Salzberg, S. L. (2008). What are decision trees? Nature Biotechnology, 26(9), 1011–1013.
   PubMed Web of Science ®Google Scholar
• Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI), 2, 1137–1143.
   Google Scholar
• Kotsiantis, S., & Kanellopoulos, D. (2006). Association rules mining: A recent overview GESTS International Transactions on Computer Science and Engineering, 32(1), 71–82.
   Google Scholar
• Kurgan, A., & Cios, K. (2004). CAIM discretization algorithm. IEEE Transactions on Knowledge and Data Engineering, 16(2), 145–153.
   Web of Science ®Google Scholar
• Lin, M., Lucas, H. C., Jr., & Shmueli, G. (2013). Research commentary—Too big to fail: Large samples and the p-value problem. Information Systems Research, 24(4), 906–917. doi: 10.1287/isre.2013.0480
   Web of Science ®Google Scholar
• Liu, J., Chen, C., Bu, J., You, M., & Tao, J. (2007). Speech emotion recognition based on a fusion of all-class and pairwise-class feature selection. In International Conference on Computational Science (pp. 168–175). Berlin Heidelberg: Springer.
   Google Scholar
• Loucaides, C. A., Plotnikoff, R. C., & Bercovitz, K. (2007). Differences in the correlates of physical activity between urban and rural Canadian youth. Journal of School Health, 77(4), 164–170.
   PubMed Web of Science ®Google Scholar
• Maletic, J. I., & Marcus, A. (2010). Data cleansing. In O. Maimon & L. Rokach (Eds.), Data mining and knowledge discovery handbook (pp. 19–32). New York: Springer.
   Google Scholar
• Merom, D., Tudor-Locke, C., Bauman, A., & Rissel, C. (2006). Active commuting to school among NSW primary school children: Implications for public health. Health and Place, 12(4), 678–687.
   PubMed Web of Science ®Google Scholar
• Mitra, R., & Faulkner, G. (2012). There’s no such thing as bad weather, just the wrong clothing: Climate, weather and active school transportation in Toronto, Canada. Canadian Journal of Public Health, 103(9), S35–S40.
   Web of Science ®Google Scholar
• Mosteller, F., & Tukey, J. W. (1968). Data analysis, including statistics. In Handbook of Social Psychology. Reading, MA: Addison-Wesley.
   Google Scholar
• Muthuri, S. K., Wachira, L.-J. M., Onywera, V. O., & Tremblay, M. S. (2016). Associations between parental perceptions of the neighbourhood environment and childhood physical activity: Results from ISCOLE-Kenya. Journal of Physical Activity & Health, 13(3), 333–343. doi: 10.1123/jpah.2014-0595
   PubMed Web of Science ®Google Scholar
• Oliver, M., Badland, H., Mavoa, S., Witten, K., Kearns, R., Ellaway, A., … Schluter, P. J. (2014). Environmental and socio-demographic associates of children’s active transport to school: A cross-sectional investigation from the URBAN Study. International Journal of Behavioral Nutrition and Physical Activity, 11(1), 70.
   PubMed Web of Science ®Google Scholar
• Oluyomi, A. O., Lee, C., Nehme, E., Dowdy, D., Ory, M. G., & Hoelscher, D. M. (2014). Parental safety concerns and active school commute: Correlates across multiple domains in the home-to-school journey. International Journal of Behavioral Nutrition and Physical Activity, 11(1), 32. doi: 10.1186/1479-5868-11-32
   PubMedGoogle Scholar
• Panter, J. R., Jones, A. P., & van Sluijs, E. M. (2008). Environmental determinants of active travel in youth: A review and framework for future research. International Journal of Behavioral Nutrition and Physical Activity, 5(1), 34.
   PubMedGoogle Scholar
• Peña, D. (2002). Análisis de datos multivariantes (24th ed.). Madrid: McGraw-Hill.
   Google Scholar
• Pont, K., Ziviani, J., Wadley, D., Bennett, S., & Abbott, R. (2009). Environmental correlates of children's active transportation: A systematic literature review. Health & Place, 15(3), 849–862.
   Web of Science ®Google Scholar
• Quinlan, J. R. (1979). Discovering rules by induction from large collections of examples. In D. Michie (Ed.), Expert systems in the micro electronic age. Edinburgh: Edinburgh University Press.
   Google Scholar
• Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Francisco, CA: Morgan Kaufmann Publisher Inc.
   Google Scholar
• R Core Team. (2016). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/.
   Google Scholar
• Rainham, D. G., Bates, C. J., Blanchard, C. M., Dummer, T. J.Kirk, S. F.& Shearer, C. L. (2012). Spatial classification of youth physical activity patterns. American Journal of Preventive Medicine, 42(5), e87–e96. doi: 10.1016/j.amepre.2012.02.011
   PubMed Web of Science ®Google Scholar
• Rissel, C. E. (2009). Active travel: A climate change mitigation strategy with co-benefits for health. New South Wales Public Health Bulletin, 20(2), 10–13. doi: 10.1071/NB08043
   PubMedGoogle Scholar
• Robertson-Wilson, J. E., Leatherdale, S. T., & Wong, S. L. (2008). Social-ecological correlates of active commuting to school among high school students. The Journal of Adolescent Health, 42(5), 486–495.
   PubMed Web of Science ®Google Scholar
• Ruiz-Ariza, A., Torre-Cruz, M. J., Redecillas-Peiró, M. T., & Martínez-López, E. J. (2015). Influencia del desplazamiento activo sobre la felicidad, el bienestar, la angustia psicológica y la imagen corporal en adolescentes. Gaceta Sanitaria, 29(6), 454–457. doi: 10.1016/j.anpedi.2012.06.005
   PubMedGoogle Scholar
• Salahuddin, M., Nehme, E., Ranjit, N., Kim, Y.-J., Oluyomi, A. O., Dowdy, D., … Hoelscher, D. M. (2016). Does parents’ social cohesion influence their perception of neighborhood safety and their children’s active commuting to and from school? Journal of Physical Activity and Health, 13(12), 1301–1309. doi: 10.1123/jpah.2016-0148
   PubMed Web of Science ®Google Scholar
• Scheepers, E., Wendel-Vos, W., van Kempen, E., Int Panis, L., Maas, J., Stipdonk, H., … Schuit, J. (2013). Personal and environmental characteristics associated with choice of active transport modes versus car use for different trip purposes of trips up to 7.5 kilometers in The Netherlands. PLoS One, 8(9), e73105. doi: 10.1371/journal.pone.0073105
   PubMed Web of Science ®Google Scholar
• Sidharthan, R., Bhat, C. R., Pendyala, R. M., & Goulias, K. G. (2011). Model for children's school travel mode choice: accounting for effects of spatial and social interaction. Transportation Research Record, 2213(1), 78–86.
   Google Scholar
• Sirard, J. R., Ainsworth, B. E., McIve, K. L., & Pate, R. R. (2005). Prevalence of active commuting at urban and suburban elementary schools in Columbia, SC. American Journal of Public Health, 95(2), 236–237.
   PubMed Web of Science ®Google Scholar
• Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36(2), 111–147.
   Google Scholar
• Timperio, A., Ball, K., Salmon, J., Roberts, R., Giles-Corti, B., Simmons, D., … Crawford, D. (2006). Personal, family, social, and environmental correlates of active commuting to school. American Journal of Preventive Medicine, 30(1), 45–51.
   PubMed Web of Science ®Google Scholar
• Van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3). ISSN 1548-7660.
   PubMed Web of Science ®Google Scholar
• Van Dyck, D., De Bourdeaudhuij, I., Cardon, G., & Deforche, B. (2010). Criterion distances and correlates of active transportation to school in Belgian older adolescents. International Journal of Behavioral Nutrition and Physical Activity, 7(1), 87.
   PubMedGoogle Scholar
• Van Hulse, J., Khoshgoftaar, T., & Huang, H. (2007). The pairwise attribute noise detection algorithm. Knowledge and Information Systems, 11(2), 171–190.
   Web of Science ®Google Scholar
• Ward, J. H. Jr.(1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244.
   Web of Science ®Google Scholar
• Willis, D. P., Manaugh, K., & El-Geneidy, A. (2015). Cycling under influence: Summarizing the influence of perceptions, attitudes, habits, and social environments on cycling for transportation. International Journal of Sustainable Transportation, 9(8), 565–579.
   Web of Science ®Google Scholar
• Woldeamanuel, M. (2016). Younger teens’ mode choice for school trips: Do parents’ attitudes toward safety and traffic conditions along the school route matter? International Journal of Sustainable Transportation, 10(2), 147–155.
   Web of Science ®Google Scholar
• Wu, J., Jiang, C., Houston, D., Baker, D., & Delfino, R. (2011). Automated time activity classification based on global positioning system (GPS) tracking data. Environmental Health, 10(1), 101.
   PubMedGoogle Scholar
• Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., … Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37.
   Web of Science ®Google Scholar
• You, M., & Li, G. (2011). Feature selection for multi-class problems by using pairwise-class and all-class techniques. International Journal of General Systems, 40(4), 381–394.
   Web of Science ®Google Scholar
• Zaki, M. J., Parthasarathy, S., Ogihara, M., & Li, W. (1997). Parallel algorithms for discovery of association rules. Data Mining and Knowledge Discovery, 1(4), 343–373.
   Web of Science ®Google Scholar
• Zhan, G., Yan, X., Zhu, S., & Wang, Y. (2016). Using hierarchical tree-based regression model to examine university student travel frequency and mode choice patterns in China. Transport Policy, 45, 55–65.
   Web of Science ®Google Scholar