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DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT

DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT Syed Monjur Murshed 24th International Cartographic Conference, Santiago, Chile 15 – 21 November, 2009. Presentation outline. Contexts and motivation Study area Proposed methodology Conclusion.

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DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT

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  1. DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT Syed Monjur Murshed 24th International Cartographic Conference, Santiago, Chile 15 – 21 November, 2009

  2. Presentation outline • Contexts and motivation • Study area • Proposed methodology • Conclusion DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 2 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

  3. Context and motivation • Detailed socio-economic data are not generally available or are purposely aggregated to avoid problems of privacy • Aggregated data need to be disaggregated at finer scale or re-aggregated at coarser scale to derive added information • The aim is to disaggregate census population data into residential land use (LU) unit and then to calculate hot water demand • Regression based dasymetric mapping of areal interpolation is applied to disaggregate population data from the municipality to the LU units (25m × 25m) • Such analysis would assist the policy makers and the energy companies to know the potential of hot water market; thus facilitate in developing sustainable energy territories DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 3 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

  4. Study area • Federal state of Baden-Württemberg, Germany • 1,111 municipalities • 44 districts • 10.7 million population • 35,751 km² area (DESTATIS 2007) Source: BMVBS 2007 DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 4 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

  5. Proposed methodology • The disaggregation methodology is divided into three main steps: • preparation of the regression dataset • development of regression model and • use of scaling techniques and estimation of population Figure: Methods of disaggregation of population data into residential LU units DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 5 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

  6. a) preparation of the regression dataset Table: Municipality characteristics • All 1,111 municipalities in BW are categorised into four different classes • 26 types of Infoterra LaND25 dataset are grouped into 4 classes • Industrial areas of CORINE data are erased from LaND25 data • Residential areas built after 1990 are not considered • Inventory dataset of municipality-wide LU information and population by overlaying municipal population data (StaLaBW, 2007) with LaND25 residential LU • This dataset is the basis for further regression analysis and modelling Table: Definition of Infoterra LaNND25 data DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 6 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

  7. b) development of regression model • Population distribution within the municipalities are not homogeneous, but is expected to relate to LU types since LU properties can be one factor in the distribution process (Flowerdew and Green, 1989) • Therefore, number of population of each municipality and the area of different types of residential LU are considered as dependent and independent variables in the linear regression models to estimate the coefficients for each LU type • The coefficients are allocated to the LU type to determine the number of population • For each category of municipalities, following regression equation is used: Where, Pi : predicted population of the municipality i, xj: residential area of the LU type j, bj: coefficient determined for residential LU type j, i : errors DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 7 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

  8. b) development of regression model Table: Estimated population densities coefficient for each residential LU classes in different municipality categories Table: Linear regressions statistics for different municipality categories DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 8 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

  9. c) use of scaling techniques and estimation of population • Scaling techniques is applied to reduce the error distribution and the influences of other parameters (Flowerdew and Green, 1989), (Yuan et al., 1997) • It is assumed that the statistical population at the municipality are highly reliable and that the estimated population of the municipality can be scaled to more refined estimation • Correction of population in each type of the LU in different municipalities are made using following equation: Where, bij: corrected population for residential LU j within the municipality i, Yi: statistical population of the municipality i, Pi: predicted population of the municipality i, xj: residential area of the LU type j, bj: coefficient determined for residential LU type j. DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 9 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

  10. c) use of scaling techniques and estimation of population • The homogeneously distributed statistical population data at the municipality level is, therefore, disaggregated into a finer scale of analysis, at LU units. • The number of population within the different LU varies significantly, depending on the size of LU, type of LU and municipality Figure. Disaggregation of statistical population data into the residential LU units in Baden-Württemberg, Germany DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 10 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

  11. Calculation of hot water demand • Hot water demand is proportional to the number of population • Average hot water consumption in Germany amounts 80m³ to 100m³ per inhabitant and per year i.e. 750 kWh/(inh.yr) to 1,070 kWh/(inh.yr) Stadtwerke Hildesheim, 2008) • Hot water demand in each LU type of every municipality can thus be calculated using the following equation DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 11 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

  12. Conclusion • The regression analysis is performed for three different categories of municipalities; therefore, the regression models are optimised • With an accuracy usually comprised between ±25%, the disaggregation of the population showed very good results in compared to other similar studies (Wu et al., 2005) • The hot water demand represents less than 15% of the total residential hot water demand in the municipalities. So the disparity of the results remains quite limited • This disaggregation methodology can also be applied for other kind of socio-demographic and energy-related data • The scale of analysis and extent of study can be further modified applied to other region, depending on the aim of study, availability of data, etc. DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 12 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

  13. References • BMVBS (2007) Wohnen und Bauen in Zahlen 2007. Bonn, Bundesministerium für Verkehr Bau und Stadtentwicklung • DESTATIS (2007) Statistisches Jahrbuch 2007 für die Bundesrepublik Deutschland. Wiesbaden, Statistischen Bundesamt Deutschland • FLOWERDEW, R. & GREEN, M. (Eds.) (1989) Statistical methods for inference between imcomparable zonal systems, Newyork, Taylor and Francis • Infoterra (2007) Infoterra LaND25 - Daten Baden-Württemberg & Puffer • STADTWERKE HILDESHEIM (2008) Wasser Eenergiespartipps. Hildesheim • STALABW (2007) Fläche, Bevölkerung - Daten zu Baden-Württemberg. Stuttgart, Statistisches Landesamt Baden-Württemberg, Germany • WU, S.-S., QIU, X. & WANG, L. (2005) Population Estimation Methods in GIS and Remote Sensing : a review. GIScience and Remote sensing, 42, 58-74 • YUAN, Y., SMITH, R. M. & LIMP, W. F. (1997) Remodeling census population with spatial information from Landsat TM imagery. Computer, Environment and Urban Systems, 21, 245-258 DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 13 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

  14. Muchas Gracias! Question? Syed Monjur Murshed European Institute for Energy Research (EIFER) Universität Karlsruhe (TH) / EDF R&D E-Mail: syed.murshed@eifer.org Web: www.eifer.org DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 14 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

  15. BACK UPs DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 15 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

  16. Infoterra LaND25 classification DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 16 24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009

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