CZECH STATISTICAL OFFICE | Na padesátém 81, 100 82 Prague 10 | czso.cz 1/X

Ing. Jaroslav Kraus, Ph.D.

Mgr. Štěpán Moravec

DISAGGREGATION METHODS FOR GEOREFERENCING INHABITANTS WITH

UNKNOWN PLACE OF RESIDENCE :

THE CASE STUDY OF POPULATION CENSUS 2011 IN THE CZECH REPUBLIC

24th October, EFGS 2013 Conference, Sofia

CZECH STATISTICAL OFFICE | Na padesátém 81, 100 82 Prague 10 | czso.cz 2/X

STARTING SITUATION

Total number of usually resident population: 10 436 560

Georeferenced inhabited building points stored in the Register

of Census Districts and Buildings managed by CZSO: 1 790 122

Georeferenced population with exact place of usual residence

(x,y coordinates): 10 343 479 High coverage of georeferenced data (above 99 %): 93 thousands inhabitants not linked to the exact place of their usual residence (0,9 % of the total census population)

10 436 560 – 10 343 479 = 93 081

But, the census data of these inhabitants are linked to the level

of statistical districts

CZECH STATISTICAL OFFICE | Na padesátém 81, 100 82 Prague 10 | czso.cz 3/X

Cause: missing, incomplete or incorrect address data

Structure of the people with unknown place of residence: homeless people people living in emergency buildings or shelters people living in buildings without final approval

Possible solution for distribution of these people into

buildings with x,y coordinates or into grids:

Application of some disaggregation method

Testing of 3 disaggregation methods via ArcGIS software

DESCRIPTION OF THE PROBLEM

CZECH STATISTICAL OFFICE | Na padesátém 81, 100 82 Prague 10 | czso.cz 4/X

Case study: small town Abertamy in the northern part of the CR

Total number of census population: 1 213 Number of not georeferenced inhabitants: 46 Total number of statistical districts: 6 Number of affected statistical districts: 6 Number of inhabited buildings: 214

CASE STUDY

CZECH STATISTICAL OFFICE | Na padesátém 81, 100 82 Prague 10 | czso.cz 5/X

1. Layer of statistical districts with number of not georeferenced inhabitants

2. Layer of population grids with number of georeferenced inhabitants

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METHOD 1: CREATING NEW RANDOM BUILDING POINTS

Creates a specified number of random point features. Random points can be generated in an extent window, inside polygon features, on point features, or along line features

Parameters:– Number of Points– Minimum Allowed Distance – Others

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METHOD 1: CREATING NEW RANDOM BUILDING POINTS

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METHOD 1: RECALCULATION OF POPULATION BY RANDOM BUILDING POINTS (1)

Recalculation of usually living by rIDOBs Abertamy community Number of rIDOBs: 47 Number of persons by rIDOBs: <1;4> dim max, min max = 4 min = 1 x = (Int((max-min+1)*Rnd+min)) __esri_field_calculator_splitter__ Počet osob = X Random number <0;1> by IDOB (e.g. ordering): dim max, min max = 1 min = 0 x = ((max-min+1)*Rnd+min) __esri_field_calculator_splitter__ Nahodne poradi (RandomOrdering) = x

Source: Using field calculator: Create Random Values, Iowa State University

CZECH STATISTICAL OFFICE | Na padesátém 81, 100 82 Prague 10 | czso.cz 9/X

1. Creating new random building points

2. Defining population (from random interval) for new random building points

3. Recalculation of limit number of inhabitants (e.g. defined by information from statistical district )

Source: ArcGIS10 Help

METHOD 1: METHODOLOGY

CZECH STATISTICAL OFFICE | Na padesátém 81, 100 82 Prague 10 | czso.cz 10/X

ArcGIS 10: method Median Center (or Mean Center, Central Feature)

METHOD 2: CREATING OF POPULATION CENTERS OF GRAVITY (1)

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MEAN CENTER (SPATIAL STATISTICS)

Identifies the location that minimizes overall Euclidean distance to the features in a dataset

Mean Center (and Median Center) are measures of central tendency

For line and polygon features, feature centroids are used in distance computations

The Case Field is used to group features for separate median center computations (e.g. by statistical districts)

Source: ArcGIS10 Help

CZECH STATISTICAL OFFICE | Na padesátém 81, 100 82 Prague 10 | czso.cz 12/X

METHOD 2: METHODOLOGY (1)1. Calculating Central Value (Mean Center, Median Center)

→ Layer of spatially weighted population centers of gravity

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2. Spatial join for linking persons with unknown place of residence into weighted center of gravity

METHOD 2: METHODOLOGY (2)

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Aim: To distribute not georeferenced population just into grids, not

into particular buildings (x,y coordinates) To respect known spatial distribution of population (based on

georeferenced population only)

Methodology:

1. To calculate a population weight of each inhabited grid

segment within affected statistical district

Population weight of grid segment i =

METHOD 3: CALCULATION OF POPULATION WEIGHTS OF GRIDS

Population number of grid segment i

Total population of statistical district j

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1. Layer of population grids with number of georeferenced inhabitants

2. Layer of population grids with relative population weight

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2. To calculate a population number distributed to each

inhabited grid segment within affected statistical district

Population weight of grid segment i * Total number of not georeferenced

persons within statistical district j

3. Rounding of the population number distributed to each

inhabited grid segment to an integer value

4. Add the number of distributed not georeferenced persons to

the initial number of georeferenced inhabitants for each grid

segment

METHOD 3: METHODOLOGY

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2. Layer of population grids with relative population weight

3. Layer of population grids with number of additionally distributed persons

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Different types of irregularities and deviations: Problem with rounding (increase or decrease of

the distributed population number)

Problem with statistical districts without inhabited buildings

Problem with grids with the same population weight

Definition of additional assumptions

and consequent manual corrections required

METHOD 3: METHODOLOGICAL ISSUES

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CONCLUSION

Pluses and minuses of method 1 and 2:

inhabitants distributed to the level of buildings

distribution according to spatial distribution of inhabited buildings

Pluses and minuses of method 3:

distribution according to spatial distribution of population

inhabitants distributed to the level of grids

All mentioned methods are used for recalculation of people with unknown exact place of residence

There is relatively enough „handworks“ to do it → some automatizations of processes are important

Finally, recalculation on single (personal) records are aim of the whole process

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Thank you for your attention.