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Location analysis for the expansion of national and public kindergarten

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Suggest location of kindergarten as Semester Research Project

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Location analysis for the expansion of national and public kindergarten

  1. 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. 2. 01 Intro 02 Analysis 03 Result 04 Implication
  3. 3. 01 Intro
  4. 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. 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. 6. 02 Analysis
  7. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 22. 03 Result
  23. 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. 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. 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. 26. 04 Implication
  27. 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. 28. Thank you Q&A

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