Linking local-climate-zones mapping to multi-resolution-analysis to deduce associative relations at intra-urban scales through an example of Metropolitan London

Abstract In this paper, we propose a systematic approach that translates geographically-based information data, into complete versatile gridded numerical information for use in comparative data analyses and atmospheric or other advanced numerical modelling studies. Intended to provide substantial added value to the wealth of data as currently collected and archived under the international initiative World-Urban-Database and Access-Portal-Tools (WUDAPT), this methodology is extensible to situations involving observed and or modelled data of different resolutions to a comparable level of resolution as a framework for deriving associative relations between areas of strong interest and attributes of topical importance. We demonstrate this methodology and approach utilizing morphological form parameters data at city block scale and their associative relationship to CO2 emissions data at borough scales. Specifically, the form-based morphology was a derivative of London's Local Climate Zones (LCZs) map information built and used in conjunction with the Multi-Resolution-Analysis (MRA) of Urban Canopy Parameters (UCPs) data.

[1]  Prashant Kumar,et al.  The role of materials selection in the urban heat island effect in dry mid-latitude climates , 2016, Environmental Fluid Mechanics.

[2]  Benjamin Bechtel,et al.  WUDAPT, an efficient land use producing data tool for mesoscale models? Integration of urban LCZ in WRF over Madrid , 2016 .

[3]  Xia Li,et al.  Estimating the relationship between urban forms and energy consumption: A case study in the Pearl River Delta, 2005–2008 , 2011 .

[4]  R. Britter,et al.  FLOW AND DISPERSION IN URBAN AREAS , 2003 .

[5]  T. Oke,et al.  Local Climate Zones for Urban Temperature Studies , 2012 .

[6]  Xia Li,et al.  Quantifying the relationship between urban forms and carbon emissions using panel data analysis , 2013, Landscape Ecology.

[7]  R. Camagni,et al.  Urban mobility and urban form: the social and environmental costs of different patterns of urban expansion , 2002 .

[8]  Hui Lin,et al.  Urban morphology detection and computation for urban climate research , 2017 .

[9]  Benjamin Bechtel,et al.  Comparison between local climate zones maps derived from administrative datasets and satellite observations , 2019, Urban Climate.

[10]  Yan Zhang,et al.  Analyzing the spatial pattern of carbon metabolism and its response to change of urban form , 2017 .

[11]  Ross Maciejewski,et al.  Urban form and composition of street canyons: A human-centric big data and deep learning approach , 2019, Landscape and Urban Planning.

[12]  Ross Maciejewski,et al.  Sky View Factor footprints for urban climate modeling , 2018, Urban Climate.

[13]  J. Cavailhès,et al.  Environmental performance of the urban form , 2016 .

[14]  I. Jolliffe.  Principal Component Analysis , 2002 .

[15]  Linda See,et al.  Pathway using WUDAPT's Digital Synthetic City tool towards generating urban canopy parameters for multi-scale urban atmospheric modeling , 2019, Urban Climate.

[16]  The WUDAPT Project: Status of Database and Portal Tools , 2017 .

[17]  I. Stewart,et al.  Redefining the urban heat island , 2011 .

[18]  Petros Mouzourides,et al.  A Scale-Adaptive Approach for Spatially-Varying Urban Morphology Characterization in Boundary Layer Parametrization Using Multi-Resolution Analysis , 2013, Boundary-Layer Meteorology.

[19]  Chuanglin Fang,et al.  Changing urban forms and carbon dioxide emissions in China: A case study of 30 provincial capital cities , 2015 .

[20]  Ruchi Choudhary,et al.  Linking the urban-scale building energy demands with city breathability and urban form characteristics , 2019, Sustainable Cities and Society.

[21]  W. Guan,et al.  An Empirical Study on the Relationship between Urban Spatial Form and CO 2 in Chinese Cities , 2017 .

[22]  Ardeshir Mahdavi,et al.  Implications of employing detailed urban canopy parameters for mesoscale climate modelling: a comparison between WUDAPT and GIS databases over Vienna, Austria , 2018 .

[23]  Fredrik Lindberg,et al.  Evaluation of Urban Local-Scale Aerodynamic Parameters: Implications for the Vertical Profile of Wind Speed and for Source Areas , 2017, Boundary-Layer Meteorology.

[24]  Daniel G. Aliaga,et al.  WUDAPT: An Urban Weather, Climate, and Environmental Modeling Infrastructure for the Anthropocene , 2018, Bulletin of the American Meteorological Society.

[25]  Linda See,et al.  Generating WUDAPT Level 0 data – Current status of production and evaluation , 2019, Urban Climate.

[26]  Ruchi Choudhary,et al.  Searching for the distinctive signature of a city in atmospheric modelling: Could the Multi-Resolution Analysis (MRA) provide the DNA of a city? , 2014 .

[27]  Ruchi Choudhary,et al.  How can a Multi-scale Analysis Guide Smart Urban Energy Demand Management? An Example from London City Westminster Borough , 2017 .

[28]  Y. Yamagata,et al.  Relationship between urban form and CO2 emissions: Evidence from fifty Japanese cities , 2012 .

[29]  Jason Ching,et al.  A perspective on urban canopy layer modeling for weather, climate and air quality applications , 2013 .

[30]  John Elder,et al.  Handbook of Statistical Analysis and Data Mining Applications , 2009 .