People's Life Indicators (living)

  • Last Update:January,12,2016 Created:January,12,2016
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Title of the dataset People's Life Indicators (living)
Provenance of the dataset http://mo161.soci.ous.ac.jp/@d/DoDStat/PLIlive/PLIlive_dataE.xml
How were the data collected/created? What was the cost? This data set was reported by the Economic Planning Agency, Japan (1999/6).
Data sharing policy Other
Data sharing policy

About data analysis and simulation

Type of data: Check all that apply. Use "Other" to specify other types so that we can include them in further updates. text number
Variable labels of dataset (the names of the variables) NUMBER OF SEVERE CRIMES (PER 100 000 PEOPLE)|NUMBER OF HOUSE FIRES (PER 100 000 HOUSES)|PROPORTION OF HOUSES OVER ORDINARY LEVEL (%)|PROPORTION OF DANGEROUS AND REPAIR-IMPOSSIBLE HOUSES(%)|NUMBER OF ACCEPTED COMPLAINTS ON ENVIRONMENTAL POLLUTION(PER100 000PEOPLE)|PROPORTION OF CONSTRUCTION OF SIDE WALK INPUBLIC ROAD (%)|NUMBER OF MAT PER PERSON (MAT)|AREA OF PARK (PER PERSON) (M2)|PROPORTION OF HOUSES RECEIVING OVER FIVE HOURS SUNSHINE (%)|PROPORTION OF HOUSES FROM WHICH AMEDICAL FACILITIY EXISTS WITHIN 500 METERS RADIUS(%)|PAVEMENT RATE OF PUBLIC ROAD (%)|RECYCLING RATE (%)|PROPORTION OF HYGIENICALLY DISPOSING OF RUBBISH (%)|AVERAGE TIME SPENT IN COMMUTING AND GOING TO SCHOOL (MINUTES)|AMOUNT OF RUBBISH PER PERSON PER DAY (G)|PROPORTION OF HOUSES FROM WHICH THE PUBLIC TRANSPORT IS AVAILABLE WITHIN 1 KM RADIUS (%)|NUMBER OF SEVERE LARCENERS (PER 100 000 PEOPLE)|HOUSE RENT PER MAT(YEN)|PROPORTION OF SEWARAGE (%)|HOME OWNERSHIP RATE(%)|PREFECTURE|LAND AREA PER RESIDENCE (M2)|PROPORTION OF HOUSES OVER MINIMUM LIVING LEVEL(%)|NUMBER OF TRAFFIC ACCIDENTS (PER 100 000 PEOPLE)
Outline of data This data set reports people's life based on 23 indicators in "living" category among 8 categories in People's Life Indicators, which were gathered and announced by the Economic Planning Agency of Japan in 1999.
Simulation process Example : Clustering Analysis
Expected outcome of the process (obtained knowledge, analysis results, output of tools) Degree of similarity between each prefectures
Anticipation for analyses/simulations other than the typical ones provided above Finding the best prefecture for opening up a new store

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