Simple areal weighting: illustration

Transcription

1 1 Using land over information to map population density Geographial disaggregation of statistial data Tallin, September 25-28, 2001 Spae Appliations Institute Diretorate General Joint Researh Centre European Commission Ispra (VA), Italy Agriulture and Regional Simple areal weighting: illustration A Simulated example of administrative units and athments B C Administrative regions 3 2 Cathments 4 1 Intersetions 2 Aim: attributing to athments values of a statistial magnitude known for administrative units Method: attributing to eah intersetion an amount proportional to the area and reaggregating per athment 1

2 Effet when the item is spatially onentrated Truth (unknown) 2.9 Estimated density from statistis by administrative region Density attributed to athments by simple areal weighting Effet when the item is homogeneous per administrative unit Item homogeneous by region Offiial statistis Density attributed to athments by simple areal weighting 2

3 Effet when the item is homogeneous per athment Item homogeneous by athment Offiial statistis Density attributed to athments by simple areal weighting Simple areal weighting with reaggregation in new units generally degrades the quality of themati maps Simple areal weighting ombined with a mask. Conentrated item and mask Offiial statistis mapped with the mask Simple areal weighting with the mask The mask improves the mapping, but reaggregating in a different system degrades it again

4 Mapping population density 7 Starting situation Population data available to the Commission only at ommune level Communes are often too large to onsider the population uniformly distributed CORINE Land Cover provides geo-referened information Other soures of data available in GISCO. Target of the study General problem: Representing statistial data in a zonal system different of the system for whih the data are known. Attributing a sensible population density to eah land over type in eah ommune Ar-view tool to view and edit results Agriulture and Regional Population density per ommune 8 Agriulture and Regional 4

5 Population density per ommune 9 Agriulture and Regional Additional Information: CORINE Land Cover 10 Agriulture and Regional 5

6 Additional Information: CORINE Land Cover 11 Agriulture and Regional Maps to be produed 12 Agriulture and Regional 6

7 Maps to be produed 13 Not perfetly aurate, but loser to reality Agriulture and Regional Population mapping 14 Areas exluded: CORINE Land Cover not available: Sweden and Finland, Frenh départements d outre-mer and some ommunes for whih a signifiant part of CORINE Land Cover is missing. Sotland: ommune boundaries ould not be obtained The Département Seine St Denis 7

8 Communes without urban area in CORINE Land Cover 15 Agriulture and Regional Size of ommunes in the EU 16 N of ommunes mean area (ha.) AT BE DE * DK ES FR GR IE IT LU NL PT Agriulture and Regional 8

9 Large ommunes 17 Agriulture and Regional Large ommunes 18 Agriulture and Regional 9

10 Large ommunes 19 Caeres: 1750 km inhab. 43 inhab/km 2 Agriulture and Regional CORINE Land Cover 20 Continuous Disontinuous ratio urban urban AT BE DE DK ES FR GR IE IT LU NL PT Total urban area (km 2 ) Agriulture and Regional 10

11 21 Communes without urban area in CORINE Land Cover: A frequent ase that should not be disregarded <1000 inhab inhab >5000 inhab Total N population N population N population N population ommunes (*1000) ommunes (*1000) ommunes (*1000) ommunes (*1000) AT DE ES FR GR IE IT LU NL PT UK total Agriulture and Regional Communes without urban area in CORINE Land Cover 22 Built area is below the CORINE Land Cover threshold (25 ha). But in many ommunes smaller polygons are represented. Geometri inonsistenies between CORINE Land Cover and SABE (possibly within the speifiations) Errors in the data. After an analysis of outliers, the only evident anomaly appeared in Seine St Denis. 11

12 Arealweighting with given oeffiients 23 X m : population in ommune m S m : area of land over type in ommune m. Y m : density of population for land over type in ommune m. Inside eah ommune Y m is assumed to be proportional to given oeffiients U for eah land over type: Y m UWm X X m m Wm Ym U SmU S U m Initial oeffiients U CORINE grouped lass 32 Continuous urban fabri 25 Disontinuous urban fabri 1 Industrial or ommerial units 1 Road and rail networks, ports, airports 1 Green urban areas, Sport and leisure 3 Non-irrigated arable land 3 Permanently irrigated land 1 Rie fields 5 Permanent rops 3 Pastures 5 Annual and permanent rops assoiated 5 Complex ultivation patterns 3 Agriulture, with natural vegetation 1 Agro-forestry areas 1 Forest and Natural vegetation They orrespond to the a priori belief of % of population living in eah CORINE lass 24 12

13 Stratified version 25 Stratum h: set of ommunes expeted to have a similar behaviour: m, m Stratum h we expet W m S X m m U h Y m U h Y Y m m S X Y Y m m m m U h Problem: How to assess the quality of the disaggregation Ideally we would need more detailed data on a sample of small pathes, and ompare the know population for eah path with the population attributed by the disaggregation proedure but suh detailed data are only available for speifi areas (not a sample) 26 13

14 Two levels of geographi aggregation 27 Assume we have ommunes and regions (groups of similar ommunes) We aggregate data at region level and disaggregate with land over data. The densities attributed are: Y r U X S We an reaggregate at ommune level: r r U * X m S m Y r Disaggregation assessment 28 We an reaggregate at ommune level: * X m And ompute the disagreement with ommune population X m * X m * m X m X If r orr m m Sm, 0 Sm S m Y r m 2 X too muh population has been attributed to ommunes where is dominant 6 14

15 Disagreement mapping by NUTS 2 and by ommune Quality of no disaggregation or disaggregation with uniform oeffiients population deviation ratio % no weight AT BE DE DK ES FR GR IE IT LU NL PT UK total Iterations to improve oeffiients 30 We an redue the orrelation with new oeffiients, for example U U r 1 r 2 X And start again to ompute the disagreement. Disagreement without disaggregation r r Disagreement after disaggregation with initial oeffiients

16 Searhing new oeffiients 31 Deviation Iterations Average oeffiients after 40 iterations (no stratifiation) 32 CORINE Urban dense Urban disontinuous Industrial and ommerial 9.56 Transport 3.71 Green urban 3.42 Arable non irrigated 2.98 Arable irrigated 3.36 Rie 2.90 Permanent rops 4.95 Pastures 2.99 Arable with permanent rops 5.40 Complex agriultural 5.92 Agriultural and natural 3.24 Agroforestry 0.86 Forest 0.94 Natural vegetation

17 Regrouping CORINE lasses 33 A luster analysis of the table of oeffiients per region suggests to group CORINE lasses: Grouped lass 1 Continuous urban fabri 2 Disontinuous urban fabri 2a Industrial or ommerial units 2b Road and rail networks, ports, airports 2b Green urban areas, Sport and leisure 3 Non-irrigated arable land 3 Permanently irrigated land 3 Rie fields 4 Permanent rops 5 Pastures 4 Annual and permanent rops assoiated 4 Complex ultivation patterns 5 Agriulture, with natural vegetation 6 Agro-forestry areas 6 Forest and Natural vegetation U a U 0.05 U U2 b 2 Stratifiation 34 Eah region (NUTS 2) has been divided in three strata: 1: Densely populated (ommune density > 2* average NUTS 2 density) 2: Less populated (ommune density < 2* average NUTS 2 density), but CORINE urban lasses present. 3: Non-urban: CORINE urban lasses absent The iterative algorithm to get disaggregation oeffiients is run for eah region*stratum with the limitation 0.1 U

18 Disagreement with grouped lasses and stratifiation Deviation Iterations Average oeffiients per stratum 36 Stratum Urban dense Urban disont Arable Perm. rops & omplex Ratio urban/others higher in stratum 1 Pasture Coeffiients for non urban onsistent aross strata. Ratio urban dense/disont. higher in stratum 1 Coeffiients an be multiplied by any onstant Forest & nat. veg Y U m W m 18

19 Average oeffiients per stratum (2) 37 Multiplying U by a onstant suh that the median value of W m is 1 in eah stratum, the oeffiients may be interpreted as median density attributed to the CORINE lass Stratum Urban dense Urban disont Arable Perm. rops & omplex Pasture Forest & nat. veg Assessment in a test site: Arezzo (Italy) Average density mapped by ommune Census setions overlaid 38 19

20 Arezzo site: Corine Land Cover 39 Arezzo site Attributed density with CORINE 40 20

21 Arezzo: real population/attributed 41 Arezzo site 42 Comparing area of over/underestimation per CORINE lass There is an assoiation overestimation-non urban, but not very lear. underestimated overestimated density ratio 0 to 0.2 to 0.4 to 0.8 to 1.25 to 2.5 to 5 > 5 Total CORINE Urban dense Urban disontinuous Transport infrast Gree urban, leisure Arable land Perm. rops and heterog Pastures Forest unpopulated Total

22 Arezzo: quality assessment of disaggregation 43 We ompare 1 X j Y Density map per ensus setion j ( truth ) j S j 1 Yj S 2,3 j 2,3 Xm j Smoothed by ommune x CLC 17 Ym Sm Sj Smoothed by ommune x CLC 7 4,5,6 Ym U X S j m m U Commune density disaggregated with EU-13 oeffiients with initial oeffiients without oeffiients (ommune density) Arezzo: quality assessment of disaggregation 44 Disagreement an be omputed: set by ensus setion 1 k X j by pixel pix 1 k i 1 k Yi Yi set 1 k pix 1k k= * * k= * * k= k= k= j k X j k=2,3 are not feasible. They measure the effet of sale and redued nomenlature. k=4 (proposed disaggregation) is lose to the optimal disaggregation with CLC + Commune data 22

23 Arezzo: heterogeneous density in the same CLC lass and same ommune 45 orine 9 lasses Urban dense Urban disontinuous Transport Green urban Arable perm_omplex Pasture_natveg Forest no population No Data Census setions Attribution of data to different spatial units 46 CORINE an help to loate more preisely population density or other statistial data (Nsurplus, CO 2 emission, et.) A modified areal weighting ombining two levels of aggregation an give reasonable weights to eah CLC lass. The proedure assumes uniform density for the same ommune and the same CLC lass. Therefore the output resolution annot go beyond CLC resolution. Comparing with more detailed data in Arezzo indiates that the oeffiients are reasonable, given the resolution. 23

24 Attribution of data to different spatial units: general onsiderations 47 If no additional (spatial) information is added, hanging zonal system generally degrades the quality of the representation. If spatial information (CLC for example) is added, disaggregating an improve the quality of data without degrading it (original data an be reomputed) Reaggregating in a different zonal system an degrade again the data. It should be done only for speifi reasons (smoothing to simplify the visual impression). Attribution of data to athments: general onsiderations 48 Hydrologial modelling generally requires knowing where things happen A total figure for the athment is not enough. The hydrologist usually needs a value for eah variable in eah ell of a grid. Reaggregation per athment has a meaning if politial deisions are taken for suh units. 24