The linkage between the lifestyle of knowledge-workers and their intra-metropolitan residential choice: A clustering approach based on self-organizing maps

This study investigates the linkage between the lifestyle and the intra-metropolitan residential choice of knowledge-workers in terms of home-ownership, location, dwelling size and building type. Data are retrieved from a revealed-preferences survey among knowledge-workers in the Tel-Aviv metropolitan area and are analyzed with self-organizing maps for pattern recognition and classification. Five clusters are identified: nest-builders, bon-vivants, careerists, entrepreneurs and laid-back. Bon-vivants and entrepreneurs differ in their dwelling size and home-ownership, although both prefer the metropolitan core. Careerists prefer suburban large detached houses. Nest-builders and laid-back are attracted to central locations, conditional on the provision of affordable medium-size dwellings.

[1]  Thomas Niedomysl,et al.  What Matters more for the Decision to Move: Jobs versus Amenities , 2010 .

[2]  Dafna Schwartz,et al.  The Regional Location of Knowledge Based Economy Activities in Israel , 2006 .

[3]  Matthew E. Kahn,et al.  Power Couples: Changes in the Locational Choice of the College Educated, 1940-1990 , 1999 .

[4]  Nick Clifton,et al.  Creative knowledge workers and location in Europe and North America: a comparative review , 2009 .

[5]  Peter Nijkamp,et al.  Spatial Moving Behaviour of Two-earner Households , 1998 .

[6]  Pascale Peters,et al.  The effects of time-spatial flexibility and new working conditions on employees’ work–life balance: the Dutch case , 2009 .

[7]  John Tomaney,et al.  The economic role of mobile professional and creative workers and their housing and residential preferences: Evidence from North East England , 2007 .

[8]  Daniel Felsenstein,et al.  Do high technology agglomerations encourage urban sprawl? , 2002 .

[9]  Alberto Petroni,et al.  MOTIVATION STRATEGIES FOR KNOWLEDGE WORKERS: EVIDENCES AND CHALLENGES , 2008 .

[10]  Theodosios Pavlidis,et al.  Integrating Region Growing and Edge Detection , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Thomas Martinetz,et al.  'Neural-gas' network for vector quantization and its application to time-series prediction , 1993, IEEE Trans. Neural Networks.

[12]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[13]  Frank van Oort,et al.  Residential amenities of knowledge workers and the location of ICT‐FIrms in the Netherlands , 2003 .

[14]  R. Florida.  The Rise of the Creative Class : And How It's Transforming Work, Leisure, Community and Everyday Life , 2003 .

[15]  Melba M. Crawford,et al.  Unsupervised multistage image classification using hierarchical clustering with a bayesian similarity measure , 2005, IEEE Transactions on Image Processing.

[16]  André Skupin,et al.  Visualizing Demographic Trajectories with Self-Organizing Maps , 2005, GeoInformatica.

[17]  R. Florida.  The Economic Geography of Talent , 2002 .

[18]  Teuvo Kohonen,et al.  Self-Organizing Maps, Third Edition , 2001, Springer Series in Information Sciences.

[19]  Barukh Kipnis,et al.  Tel Aviv, Israel - a world city in evolution: urban development at a deadend of the global economy , 2004 .

[20]  M. Ben-Akiva,et al.  The Use of the Life-Style Concept in Travel Demand Models , 1983 .

[21]  Peter Lee,et al.  The Creative Economy and Social Sustainability: Planning for Opportunity and Growth , 2009 .

[22]  Clifford R. Kern,et al.  A model of workplace and residence choice in two-worker households , 1997 .

[23]  Diane-Gabrielle Tremblay,et al.  What attracts and retains knowledge workers/students: The quality of place or career opportunities? The cases of Montreal and Ottawa , 2010 .

[24]  Høgni Kalsø Hansen,et al.  Knowledge Bases, Talents, and Contexts: On the Usefulness of the Creative Class Approach in Sweden , 2009 .

[25]  Peter Nijkamp,et al.  Multidimensional urban sprawl in Europe: A self-organizing map approach , 2011, Comput. Environ. Urban Syst..

[26]  Klaus R. Kunzmann,et al.  The Strategic Dimensions of Knowledge industries in Urban Development , 2009 .

[27]  Patricia L. Mokhtarian,et al.  What type of vehicle do people drive? The role of attitude and lifestyle in influencing vehicle type choice - eScholarship , 2004 .

[28]  Christian Reiner,et al.  Brain competition policy as a new paradigm of regional policy: A European perspective , 2010 .

[29]  Milton Sousa,et al.  Knowledge workers, servant leadership and the search for meaning in knowledge‐driven organizations , 2010 .

[30]  Subhash Sharma.  Applied multivariate techniques , 1995 .

[31]  Shlomo Bekhor,et al.  Methodology for Exploratory Analysis of Latent Factors Influencing Drivers' Behavior , 2005 .

[32]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[33]  Tan Yigitcanlar,et al.  Attracting and retaining knowledge workers in knowledge cities , 2007, J. Knowl. Manag..

[34]  Jean-Claude Thill,et al.  Visual Data Mining in Spatial Interaction Analysis with Self-Organizing Maps , 2009 .

[35]  Otto Raspe,et al.  The Knowledge Economy and Urban Economic Growth , 2006 .

[36]  A. Green.  A Question of Compromise? Case Study Evidence on the Location and Mobility Strategies of Dual Career Households , 1997 .

[37]  J. Brown,et al.  Complexcities: locational choices of creative knowledge workers , 2009 .

[38]  V. Mathur,et al.  Do amenities matter in attracting knowledge workers for regional economic development , 2005 .

[39]  Ronald R. Hagelman.  Visualizing Demographic Trajectories with , 2005 .

[40]  Patricia L. Mokhtarian,et al.  What Affects Commute Mode Choice: Neighborhood Physical Structure or Preferences Toward Neighborhoods? , 2005 .