Through analyzing geographical big data, the study identified suitable locations to expand national and public kindergartens in Seoul. Data on population, birth rates, income levels, and existing kindergarten locations and densities were analyzed at the dong level and overlaid to determine high priority areas. Two specific locations in Gangseo-gu were identified as suitable sites. While the analysis provides guidance, additional criteria may be needed for areas with high need but no available buildings, like Sanggye-dong. The results can help reduce regional bias and inform Seoul's kindergarten expansion planning and budgeting.

1. Location analysis for the expansion of
national and public kindergarten
Hongik University – Urban engineering
Gyeoul Jung · Sungyeol Choi
Data Analysis Slide making & Presentation

2. 01
Intro
02
Analysis
03
Result
04
Implication

3. 01
Intro

4. Geographical Big Data Analysis
Background and Purpose
Rate of national and public kindergarten
10.8% at 2011 → 15.5% at 2016
Parents still feel that there is not much.
According to the statistics of Seoul City in 2014, there
are 243k children in the kindergarten, of which only
58k children are in public kindergarten. Only one of
every four people goes to a public kindergarten.
In addition, the waiting period could be up to 3 years
due to lack of facilities.
↓
Through the analysis, it is aimed to build
national and public kindergartenon right site.

5. Geographical Big Data Analysis
Data
Name Source Year Unit
Birth rate Statistics Korea 2015 Gu
Population 공유 활용 플랫폼데이터 2015 Dong
Kindergarten location 공유 활용 플랫폼데이터 2015 Seoul-si
Income Seoul SW data 2015 2015 Dong
Neighborhood facility Building DB present Seoul-si
Tool
General: QGIS, ArcGIS
Data processing: Python
* Si > Gu > Dong

6. 02
Analysis

7. Flow chart
child density Kindergarten density
child population count
Top 10 Dong
neighborhood
facility
Euclid
Distance
Fianl Site
Geographical Big Data Analysis
population income
kindergarten
location
birth rate
kindergarten
location

8. Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →
Data Processing
1. Population(Dong)
Field name Detail
Base year
Base month
Sex male, female, all
Age
0-4
5-9
…
Population N
Dong Dong Code
Gu
Dong Code Population
XXXXXXX XX
Child Population by Dong
Dong Code Child Density
XXXXXXX XX
Child density by Dong
※ Child: 0-9 years old
※ ※ When calculating the area, the area unit is km2
Dong BASEMAP
Table join of BASEMAP
and Child Density based
on Dong Code.
𝐷𝑒𝑛𝑠𝑖𝑡𝑦 =
𝐶ℎ𝑖𝑙𝑑 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
𝐷𝑜𝑛𝑔 𝑎𝑟𝑒𝑎
※ Use CALCULATE GEOMETRY
Filter the data
using python
Geographical Big Data Analysis

9. 1. Population(Dong) → Child Density
42-545
545-874
874-1128
1128-1445
1445-1688
1688-1942
1942-2246
2246-2565
2565-3059
3059-6622Child Density
( person / km2 )
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →

10. 2. Income
Field name Detail
Dong Code
Category
All
Male
Female
20-29
30-39
…
Income N
Income level
Dong Code Income
XXXXXXX XXXX
Dong BASEMAP
Calculate average income
by dong using python
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →
※ income unit is ten thousand won (≒ $10)
Table join of
BASEMAP and Income
based on Dong Code.

11. 2. Income
Income
3125-3441
3441-3566
3566-3658
3658-3762
3762-3894
3894-4020
4020-4207
4207-4428
4428-4852
4852-6447
( 10 thousand won )
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →

12. 3. Kindergarten Location
Field name
Address
Director
…
Building area
Type
NEW FILED Count
Dong Code N
Dong Code Density
XXXXXXX N
Dong BASEMAP
Number of kindergarten by Dong
Kindergarten Density
Count number of kindergarten
by Dong using spatial Join
𝐷𝑒𝑛𝑠𝑖𝑡𝑦 =
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑘𝑖𝑛𝑑𝑒𝑟𝑔𝑎𝑟𝑡𝑒𝑛
𝐷𝑜𝑛𝑔 𝑎𝑟𝑒𝑎
※ Use CALCULATE GEOMETRY
Location of national
and public kindergarten
TABLE JOIN
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →

13. 3. Kindergarten Location
→ Kindergarten Density
0.00-0.42
0.42-0.81
0.81-1.14
1.14-1.37
1.37-1.61
1.61-1.91
1.91-2.40
2.40-3.01
3.01-3.87
3.87-11.45
( Count / km2 )
Kindergarten Density
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →

14. 4. Birth rate
Gu name Birth rate
XXXXXXX X.XXX
Get birth rate data
by Gu from Statistics Korea
Add ‘Gu Code’ field to attribute table using python
※ Gu Code = First 4 characters in Dong Code
If ABCDEXX is Dong Code, ABCDE is Gu Code
Gu name Birth rate Gu Code
XXXX X.XXX ABCDE
Dong BASEMAP
Dong Code … Gu Code
ABCDEXXXX XXX ABCDE
Add ‘Gu Code’ field to Dong BASEMAP table
※ Use subtract(Dong Code, 1, 5) function
Table Join by Gu Code
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →

15. 4. Birth rate
0.8130-0.8570
0.8570-0.9130
0.9130-0.9600
0.9600-1.0030
1.0030-1.0080
1.0080-1.0120
1.0210-1.0360
1.0360-1.0850
1.0850-1.0960
1.0960-1.1690
( person )
Birth rate
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →

16. CLASSIFY
Child Density (30%)
Brith rate (20%)Kindergarten Density (30%)
Income (20%)
8-9
7-8
6-7
5-6
4-5
3-4
2-3
1-2
0-1
CLASSIFY Child Density ↑
Kindergarten Density ↓
Income ↓
Brith rate ↑
OVERLAY
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →
Suitable
Child Density ↓
Kindergarten Density ↑
Income ↑
Brith rate ↓
Unsuitable

17. Suitable is blue color, Unsuitable is red color
Northeast and Southwest regions are relatively in need of expansion of national and public kindergarten.
OVERLAY
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →

18. Derived Top10 Dong area with high necessity for the expansion of kindergarten
Intermediate Result – TOP10 Dong
TOP10
Sanggye 9 dong, Nowon-gu
Sanggye 6,7 dong, Nowon-gu
Hwagok 1 dong, Gangseo-gu
Hwagok 2 dong, Gangseo-gu
Hwagok 4 dong, Gangseo-gu
Hwagok bon dong, Gangseo-gu
Myeonmok-dong, Jungnang-gu
Daejo-dong, Eunpyeong-gu
Gocheok 2 dong, Guro-gu
Gaebong 2 dong, Guro-gu TOP 10 Dong
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →

19. Analysis Hwagok 1,2,4,bon dong
Euclid Distance Analysis
EUCLID DISTNACE
(a) Input source data: location of kindergarten - Cell Size : 30m X 30m
(b) Extract Top 40%
1 Dong
2 Dong
4 Dong
Bon-Dong
→ →
(a) (b)
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →

20. Kindergarten can be built only in the Neighborhood facility area.
Overlay with Neighborhood facility building
→
Building site that can be built
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →
1 Dong
2 Dong
4 Dong
Bon-Dong

21. Final result is intersection of between Euclid distance top 40% and building site.
+
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →
Overlay with Neighborhood facility building

22. 03
Result

23. Intersection with area and building
Final result
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →

24. Actual location
A
B
A
B
A
B
28, Gomdallae-ro 51-gil, Gangseo-gu, Seoul
41, Gomdallae-ro 53-gil, Gangseo-gu, Seoul
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →

25. The remaining Dong
Gocheok 2 dong, Guro-guGaebong 2 dong, Guro-guDaejo-dong, Eunpyeong-guMyeonmok-dong, Jungnang-gu
Sanggye 6,7,9 dong have no intersect area
Geographical Big Data Analysis
Fianl result→Overlay with building→
Intermediate
result
Euclid Distance Analysis→Overlay →Classify →Data Processing →

26. 04
Implication

27. Implication
The results of the analysis can be used to expand a kindergarten in Seoul.
It is expected to reduce that the regional bias in the expansion
There is a need to provide additional criteria when a kindergarten is needed
but the building doesn’t exist like Sanggye-dong
Geographical Big Data Analysis
Number BudgetYear
Expansion Plan in Seoul

28. Thank you
Q&A