disaggregation of regional population data for residential hot water demand assessment
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
EUROPEAN INSTITUTE FOR ENERGY RESEARCH
EUROPÄISCHES INSTITUT FÜR ENERGIEFORSCHUNGINSTITUT EUROPEEN DE RECHERCHE SUR L’ENERGIEEUROPEAN INSTITUTE FOR ENERGY RESEARCH
DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND
ASSESSMENT
Syed Monjur Murshed
24th International Cartographic Conference, Santiago, Chile15 – 21 November, 2009
DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 2
24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009
Presentation outline
• Contexts and motivation
• Study area
• Proposed methodology
• Conclusion
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24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009
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
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24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009
Study area
• Federal state of Baden-Württemberg, Germany• 1,111 municipalities • 44 districts • 10.7 million population • 35,751 km² area (DESTATIS 2007)
0%
5%
10%
15%
20%
25%
Baden-Württemberg Germany
Repartition of municipalities per size
Source: BMVBS 2007
0%
5%
10%
15%
20%
25%
< 20
020
0 - 5
0050
0 - 1
000
1000
- 20
0020
00 -
3000
3000
- 50
00
5000
- 10
000
1000
0 - 2
0000
2000
0 - 5
0000
5000
0 - 1
0000
0
1000
00 -
2000
00
2000
00 -
5000
00>
5000
00
Baden-Württemberg Germany
Repartition of population per municipality size
DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 5
24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009
Proposed methodology
Figure: Methods of disaggregation of population data into residential LU units
The disaggregation methodology is divided into three main steps:
(a) preparation of the regression dataset (b) development of regression model and (c) use of scaling techniques and estimation of population
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24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009
a) preparation of the regression dataset
Municipality category
Number of inhabitants
Numbers of municipalities
Municipality 1 0 to 2,000 194
Municipality 2 2,001 to 20,000 820
Municipality 3 20,001 to 200,000 93
Municipality 4 More than 200,001 4
• 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
Infoterra LaND25
(LU)
Description Proposed LU
LU1 Extremely dense urban Code 12
LU2 High buildings in extremely dense urban
LU3 Dense urban Code 34
LU4 High buildings in dense urban
LU5 Urban fabric Code 5
LU6 Village and suburban Code 6
Table: Municipality characteristics
Table: Definition of Infoterra LaNND25 data
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24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009
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:
n
jjji ixbP
1
)(
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 8
24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009
b) development of regression model
Proposed LU
Infoterra LU
Coefficient (weight/m²)
Municipality 1 Municipality 2 Municipality 3
Code 12 LU1 - - 0.078166
LU2 - - 0.078166
Code 34 LU3 0.004616 0.013765 0.013696
LU4 0.004616 0.013765 0.013696
Code 5 LU5 0.002645 0.006549 0.009145
Code 6 LU6 0.001991 0.002849 0.003376
Table: Estimated population densities coefficient for each residential LU classes in different municipality categories
Linear Regression statistics
Municipality 1 Municipality 2 Municipality 3
Intercept/error 267.3030702 405.8836834 791.357419
Multiple R 0.831805 0.891197 0.934276
R Square 0.691899 0.794232 0.872872
Adjusted R Square 0.687034 0.793476 0.867094
Standard Error 329.6397 1821.173 9213.72
Observations 194 820 93
Table: Linear regressions statistics for different municipality categories
DISAGGREGATION OF REGIONAL POPULATION DATA FOR RESIDENTIAL HOT WATER DEMAND ASSESSMENT 9
24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009
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:
jji
iij xb
P
Yb
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 10
24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009
c) use of scaling techniques and estimation of population
Figure. Disaggregation of statistical population data into the residential LU units in Baden-Württemberg, Germany
• 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
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24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009
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
inhyinh.a
kWh w
a
kWh HWD ijij
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24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009
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.
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24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009
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
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Muchas Gracias!
Question?
Syed Monjur MurshedEuropean Institute for Energy Research (EIFER)Universität Karlsruhe (TH) / EDF R&D
E-Mail: [email protected]: www.eifer.org
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24th International Cartographic Conference, Santiago, Chile | 15 - 21 November, 2009
BACK UPs
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Infoterra LaND25 classificationInfoterra LaND25 classification New classification
LU Code Description Description
1 Extremely dense urban
Residential
2 High buildings in extremely dense urban
3 Dense urban
4 High buildings in dense urban
5 Urban fabric
6 Village and suburban
7 Urban green
Non-residential
8 Sealed areas
9 Industrial and commercial buildings
10 Airport buildings
11 Urban bridges
12 Non-urban bridges
13 Mineral extraction site
14 Non-urban sealed area
25 Roads/Railways through forest
26 Teleatlas Roads
15 Coniferous forest
Vegetation
16 Deciduous forest
17 Mixed forest
18 Spacious woodland
19 Agriculture, Grassland
20 Natural open areas
21 Rock, snow
Water and rock22 Water bodies
23 Stream courses
24 Sea and oceans