statistics as a service - efgs · county –municipality –abc grouping - sorting number –serial...
TRANSCRIPT
Statistics as a service
Fredrik Garli
Gunilla Sandberg
DSA
SAMS → DSA
• Why a new division?
• SAMS didn´t age very well
• Too many areas in several
municipalities
• The Stockholm/Gothenburg
syndrome
• Crosses through localities
• No consideration of natural barriers
Demands and criterias
• Municipality bound
• Permanent
• Localities considered
• Make use of electoral districts
• Population 1 000 – 3 000 per area
• 2 electoral
districts
• 2 localities
• Crosses locality
Decision:
One area for the
largest locality.
One area for the
surroundings.
Example: Skinnskatteberg
SAMS Electoral districts DSA
Homogeneity
SAMS DSA
Population Number of areas Inhabitants Population Number of areas Inhabitants
0 191 0 0 0 0
1-499 3 280 744 818 1-499 0 0
500-999 2 197 1 608 042 500-999 308 276 244
1000-1999 2 391 3 420 998 1000-1999 4 365 6 780 605
2000-4999 987 2 825 542 2000-4999 1 312 2 923 051
5000-9999 120 817 936 5000-9999 0 0
10000-w 40 561 068 10000-w 0 0
Västerås
DeSO
0
500
1000
1500
2000
2500
3000
3500
4000
4500
1 4 7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
55
58
61
64
67
70
73
76
79
82
85
88
91
94
97
100
103
106
109
112
115
118
121
124
127
130
0
500
1000
1500
2000
2500
1 4 7
10
13
16
19
22
25
28
31
34
37
40
43
46
49
52
55
58
61
64
67
SAMS
SAMS DeSO
Befolkning
Områden
Increase of populationHammarby sjöstad (Stockholm)
1995:
489 inhabitants
2016:
18 411 inhabitants
Increase of populationHammarby sjöstad (Stockholm)
1995:
489 inhabitants
2016:
18 411 inhabitants
Less areas in Gothenburg
DSASAMS
Stockholm and Gothenburg
SAMS DSA
Areas Pop/area Areas Pop/area
Stockholm 128 7 367 544 1 720
Göteborg 876 635 306 1 820
DSA per municipality
Stockholm
UppsalaLinköping
Norrköping
Jönköping
Växjö
Malmö
Göteborg
Örebro
KarlstadVästerås
FalunGävle
Sundsvall
ÖSD
Umeå
Luleå
Borås
LundHelsingborg
The DSA codes
County – Municipality – ABC grouping -
Sorting number – Serial number – Place of reserve
Some final notes
• Published 2018-01-01
• 5 985 DSA
• DSA only comes with a code
• One size does not fit all
• Areas including a few statistical tables as open geodata
• Input to an ongoing project – analyzing segregation
Open data for local statistics
Background
• New open data policy in Statistics Sweden
• Statistical data on local areas as open data
• Until now, mainly commission based services
• Introduction of DSA in official statistics
• Until now, municipalities has been the lowest level (with a few
exceptions)
• A service for user-defined areas of interest (”Statistics as a Service”)
The approach
Co
mp
lexity
User
The tourist
The problem-solver
The analyst
.
User-stories
• Journalist
• Historian
• Property owner
• Municipality analyst
• Supermarket owner
Presentation tool
• Connected to SSD
• Dynamic map, tables and charts
• Searchable from SSD or presentation
tool
• Geographic sprawl and changes over
time
• Same divisions as for “the official
statistics” - counties, municipalities and
DSA.
New map in Regina
• Better performance
• Responsive
• Searchable on all regional divisions,
even historical
• Searchable on address
• Ability to measure a distance
Regina-map with statistics
• Ability to create your own area using
DSA
• Result is shown in a table
• Data is retrieved from SSD or
microdata
Flexible tool
• Create an area by choosing a
radius around an address
• Create a zone around a road
etc.
• Result is shown in a table
• Statistics for the latest
available year
Statistical areas - DSA• Population
• by age
• by sex
• by civil status
• by citizenship
• by country of birth
• by Swedish/foreign
background
• change
• Households
• by type of household
• Income
• Population aged 20+
> by total income from
employment and business
> by net income
> by purchasing power
> by income structure
• Share of people with low
economic standard by age
• Labour market
• Gainfully employed population
aged 16+, by industry
• Gainfully employed population
aged 16+, by sector
• Gainfully employed daytime
population aged 16+
• Population aged 20-64, by
employment
• Education
• Population aged 25-64, by
educational level
• Properties and dwellings
• Real estate by type of property
• Population by type of real
estate
• Population by form of tenure
• Number of dwellings by form of
tenure
• Vehicles
• Vehicles owned by a natural
person by type of vehicle and
status
• Vehicles in use by fuel
Statistical areas – flexible tool
• Population
• by age
• by sex
• by Swedish/foreign
background
• Households
• by type of household
• Income
• Median income from
employment and business
for population aged 20+
• Median purchasing power for
population aged 20+
• Labour market
• Gainfully employed daytime
population aged 16+
• Population aged 20-64, by
employment
• Education
• Population aged 25-64, by
educational level
• Properties and dwellings
• Most common form of tenure
• Population by type of real
estate
• Vehicles
• Number of vehicles owned by
a natural person
Challenges
• Privacy and disclosure controls
Proposed solutions for privacy protection
• All micro data need to be aggregated
to grid cells before query (to avoid
problems with geographical
differencing)
• Grid cells invisible for users
• Thresholds and suppression of small
values + noise
• Use of intervals and median values
(for sensitive variables)
• Fixed radius for point-of-interest
Challenges
• Privacy and disclosure controls
• Data security
• Performance
• Design
• Effective linking
It is possible!
Thank you!
