Type of data: Check all that apply. Use "Other" to specify other types so that we can include them in further updates. |
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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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