Building stock data in Urgenche

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Revision as of 02:09, 3 October 2011 by Jouni (talk | contribs) (description improved)
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Question

What data should be collected and how should it be handled in Urgenche so that

  • the necessary information about city structure and other city-specific information for city-level assessments is collected (additional guidance exists for energy use and traffic),
  • the guidance helps the information collection work without excluding any useful data sources or formats?

Answer

1) The city-partners deliver their data in the original format, whatever it is that the city uses.

  • The data is uploaded to Heande file management system (M-files) by THL (or by the city partner after proper training).
  • The data collected is continuously available for all project partners also about city studies they are not involved in.
  • The original data is managed and manipulated so that the information described below can be extracted.


2) The data is converted into "our best estimate about the issue at hand".

("Our" means the project partners.)
Uncertainties are explicitly considered. In principle, all information is given in a probabilistic format using probability distributions. However, there are a few rules to make this principle easy to follow:
  • If the information is known with practical certainty (i.e., the uncertainty involved probably does not have any importance in the results or conclusions), information is given as deterministic values.
  • --# : Should we use Aguila in Urgenche? It could be attached to Opasnet. It might help the work a lot. --Jouni 05:09, 3 October 2011 (EEST)
  • If the information is uncertain, a range is described with upper and lower plausible values. The plausible range equals the 90 % subjective confidence interval, or in other words it is 5 % chance that the actual value is actually above the range, and 5 % chance that it is below.
  • The true value is expected to be anywhere within the range with equal probability, i.e. a uniform distribution is assumed.
  • Only in rare cases more complex probability structures are needed than plausible ranges.


3) The city is divided into "blocks" and time points.

  • The blocks are based on some hierarchical, exclusive, and exhaustive structure that is useful for that particular city, if possible a structure that the city itself uses.
  • Each block can be divided into sub-blocks as small as necessary for the purposes of an assessment.
  • The data entered is always bound to a particular block at some level of coarseness. This may and will vary from one data to another. There is no need to go any more specific than necessary for the purpose of the assessment. Many data can be at the city-level, such as monthly ambient temperatures.
  • The same hierarchical, exclusive, and exhaustive approach applies to time points as well. All data is bound to a particular time point, or more specifically a period with a start and an end, during which the data is valid. The length of time periods depends on the needs of the assessment. For example, some data may be specific for a season while some apply for a range of years.
  • The information should cover the whole city and the whole time period since the beginning. This information is then corrected and updated during the project until the partners believe that the city case study produces reliable results for the questions asked.


4) Data needs (per city block and time point)

Information on land use (m2)

  • floor area
  • green area
  • building area
  • asphalt/paved area
  • other land area
  • water area

Information on building stock (% of floor area)

The following data needs to be expressed possibly in terms of age of building.
  • block of flats
  • row house
  • detached house
  • office
  • industry
  • public, shops etc.
Other data
  • Monthly temperature (degrees C)
  • Monthly rainfall (mm)
  • Monthly apparent temperature (degrees C)
  • Average indoor temperature (degrees C)
  • Population by age (#)

Possible indoor environment quality (IEQ)indicators (%)

  • Fraction of buildings with moisture damage
  • Fraction of buildings with indoor smoking
  • Fraction of buildings with indoor combustion
  • Fraction of buildings exceeding the selected level of indoor concentration of specific chemicals, e.g. formaldehyde, solvents etc.

Rationale

  • We do not want a random person in the city administration to try and adjust the data according to an Urgenche survey. The interpretation of the data must happen within the project, not before it enters the project.
  • The selection of indoor environment quality indicators will be made in collaboration with WP6.

See also

References


Related files

<mfanonymousfilelist></mfanonymousfilelist>

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