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Merging statistics and geospatial information Demography / Commuting / Spatial planning / Registers
Mirosław MigaczChief GIS Specialist
Central Statistical Office of PolandINSPIRE Conference 2014: Inspire for good governance
Aalborg, June 17th 2013
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Agenda
The aim
The team
The tasks• Spatial visualization of demographic data• Enterprise address spatialization• Commuting statistics• Statistical indicators for spatial planning
The results• Conclusions
The aim
3
•Population and Housing Census 2011 results
•other statistical datasets possessed by CSO
Geospatial analysis with use of:
•Spatial address databases (maintained within official statistics)
•Database of Topographic Objects (acquired from the mapping agency)
Evaluation of reference materials
in geostatistics production process:
The team
4
Programming and Coordination of Statistical Surveys Department @ CSO, Warsaw• Amelia Wardzińska-Sharif• Janusz Dygaszewicz• Mirosław Migacz• Magdalena Pączek-Borowska• Agnieszka Nowakowska
Urban Statistics Centre @ SO Poznań• Sylwia Filas-Przybył• Maciej Kaźmierczak• Dawid Pawlikowski
Regional and Environmental Surveys Department @ CSO, Warsaw• Marek Pieniążek• Robert Buciak
Statistical Computing Centre, Łódź• Radosław Jabłoński
SPATIAL VISUALIZATIONOF DEMOGRAPHIC DATA
Spatial visualization of demographic data
Source data• attribute data• spatial data
Methods of aggregation to various statistical units• 1 km x 1 km grid• Cadastral units• Statistical regions• Census enumeration areas
Cartographic presentation of the results
Source data
Attribute
• Tables with population distribution data acquired from the Population and Housing Census 2011:• Person ID• X, Y coordinates (acquired
from spatial address databases created and maintained within official statistics)
• 39 tables (one for each million people)
Spatial
• Boundaries of statistical regions and census enumeration areas (spatial address databases)
• Cadastral units (mapping agency)
• Kilometer grid – Grid_ETRS89_LAEA_PL_1K (European Forum for Geography and Statistics)
1 km x 1 km grid
• Grid_ETRS89_LAEA_PL_1K – the european INSPIRE grid• Cell coordinates – lower left corner• Aggregation of persons to specific grid cells possible w/o GIS
software (Visual Basic for Applications used here for example)
1 km x 1 km grid
• Number of persons in each grid cell calculated with ArcGIS (Dissolve tool), though any other database software could be used
• The operation was conducted separately for each of the 39 tables
Cadastral units
• Aggregation to irregular division of space requires GIS software
• Environment: ArcGIS file geodatabase• Spatial operations on a feature class with 38,5 mln objects
exceed RAM capabilities of workstations and servers
Cadastral units
Back to 39 separate tables >> need for automation
Use of Python scripting with the arcpy module that contains all ArcGIS tools
• The script was processing 39 datasets– Spatial join of the 1st dataset to the
geometry of cadastral units (with calculation of total population) – the initial dataset
– For each subsequent spatial join the current calculated population was added to the total population for each cadastral unit
Statistical regions and census enumeration areas
The same tools that were used for cadastral units (ArcGIS, Python)
A slightly different method of cyclic dataset processing:
• statistical regions / census enumeration areas were spatially joined to datasets with persons 39 times >> 39 temporary feature classes
• 39 feature classes merged into one >> 1 feature class with 39 duplicate geometries for each statistical region / census enumeration area
• deduplication of the geometries with total population calculation for each geometry (Dissolve tool in ArcGIS)
Data aggregation – conclusions
• Point data aggregation to grids can be done without GIS software – any database software with e.g. VBA is sufficient
• Point data aggregation to an irregular division of space requires GIS software
• Processing of huge datasets requires automation, which can be acchieved with Python scripting:– requires script preparation and testing on a data sample– all processes can be run on a separate machine / server and they do
not require the operator’s attention
Cartographic presentation of the results
• 1 km x 1 km grid – total population in each grid cell (= population density)
• Cadastral units, statistical regions, census enumeration areas – choropleth maps of population density
Classifications(5 classes)
average value as the center of the middle class
quantiles
Colour scales
2-color gradient
monochromatic
1 km x 1 km grid
1 km x 1 km grid
1 km x 1 km grid
1 km x 1 km grid
Cadastral units
Census enumeration areas
Cadastral units – quantiles
Census enumeration areas – quantiles
Cadastral units vs census enumeration areas (quantiles)
Quantiles – conclusions
• Significant differences between quantile presentations:– For the 1 km x 1 km grid a separate class for „0” was created– Huge differences in classification between cadastral units and census
enumeration areas due to these divisions having been created for different purposes:• Cadastral units for legal management of land ownership• Census enumeration areas for the purpose of conducting censuses (size
dependant on the population count)
ENTERPRISE ADDRESS SPATIALIZATION
Source data
Attribute•Social insurance registers•Taxpayers register•Inland revenues database•Statistical registerof enterprises
Spatial•Spatial address databases (maintained within official statistics)•Databaseof Topographic Objects (acquired from the mapping agency)
Enterprise address spatialization
Pairing „as is”(62%)
Address number
simplification (e.g. 3A -> 3)(5,9%)
No address point
(nearest address number)(18,6%)
No address number (address point on
same street or locality centroid)
(1,8%)
No street ID
(locality centroid)
(3,6%)
Other cases
(locality centroid)
(8,1%)
Address descriptive information paired with:
• address points from the Spatial Address Databases• address points from the Database of Topographic Objects
COMMUTING STATISTICS
Commutera person whose employer’s registered office is outside the administrative borders of the gmina (municipality, LAU2) of residence
Commuting statistics
Source data
• attribute data• spatial data
Actions
• Directions of population movements related to employment• Commuting to/from Poznań• Commuting within voivodships
Cartographic presentation of the results
Source data
Attribute•Tables with demographic data acquired from the Population and Housing Census 2011:•Person ID•Age•Gender•Dwelling address and X, Y coordinates•Workplace address and X, Y coordinates•Income•Economic activity classification•Fact of commuting•3,1 million records
Spatial•Boundaries of the territorial division of the country•Spatial Address Databases (source of dwelling coordinates and boundaries of statistical regions and census enumeration areas)•Spatialized enterprise addresses (source of workplace coordinates)•Kilometer grid – Grid_ETRS89_LAEA_PL_1K (European Forum for Geography and Statistics)
Percentage of commuters in the number of employees
statistical unit: powiat (county) (LAU1)
Surplus – arriving / departing to work
statistical unit: 1km x 1km grid (ETRS89-LAEA)
Surplus – arriving / departing to work
area: city of Poznań and surroundingsstatistical unit: 1km x 1km grid (ETRS89-LAEA)
Quotient of commuting flows
area: city of Poznaństatistical unit: census enumeration area
Arriving / departing to work
area: city of Poznaństatistical unit: 250m x 250m grid (ETRS89-LAEA)
Percentage of people commutingto voivodship (NUTS2) capitals
statistical unit: gmina (municipality) (LAU2)
STATISTICAL INDICATORSFOR SPATIAL PLANNING
Statistical indicators for spatial planning
Source data
• spatial data
Scope
• selected administrative units
Aims
• Source data usability analysis for purposes of creating statistical indicators for spatial planning• methodology for statistical indicators describing building density• methodology for statistical indicators describing road density
Cartographic presentation of the indicators
Source data
Spatial
•Database of Topographic Objects (buildings and road network)•cadastral data•ortophotomap•Boundaries of statistical regions and census enumeration areas (spatial address databases)•Boundaries of the territorial division of the country
Scope27 gminas (LAU2) from 4 powiats (LAU1) located north of Warsaw
Source data evaluation
• comparing the content of randomly selected grid cells within the Database of Topographic Objects with the ortophotomap
• roads - 59 grid cells sampled out of a total number of 1185– 79,7% cells with total compliance– rest with compliance > 75%
• buildings - 135 grid cells sampledout of a total number of 2697– 44,5% cells with total compliance– 37,8% cells with compliance > 75%– rest majorly with compliance > 50%
• gaps found mainly in urban areas
omissions in the building layer
Building density indicator (%)
areasurveyP
areabuildingtotalP
ratiodensitybuildingW
P
PW
P
Z
Z
P
ZZ
%100
Building density indicator
grid cell with the biggest number of buildings
grid cell with the highest building density ratio
town of Ząbki
city of Wołomin
Road density indicator (km/km2)
areasurveyP
lengthroadtotalD
P
DW
P
D
P
DD
Road density model (m/km2)
CONCLUSIONS
Conclusions
census results referenced to a point (X,Y)
huge opportunity for spatial analyses
geostatistical products that reflect user
needs
high demand for
demographic data lower than LAU2
level
positive reception of project
results
SUCCESS
Conclusions
• The project outcome will have a strong impact on future developments of the Geostatistics Portal (incl. INSPIRE services)
• Wednesday, June 18th, 16:00 @ Room 4„Geostatistics Portal – the multitool for statistics on maps”(session: „Maps, Stats and Observation Data”)
GEO.STAT.GOV.PL
Merging statistics and geospatial information Demography / Commuting / Spatial planning / Registers
Mirosław MigaczChief GIS SpecialistCentral Statistical Office of Poland
@mireslav
www.linkedin.com/in/migacz
www.slideshare.net/MirosawMigacz