smart map — profiled customized path
with social data & spatial analysis
: through this project, people can be guided optimal path by communicating various conditions including spatial analysis and informative dataset around your place
background
- google maps path algorithm is too constrained and only one-direction based on fastest path
- currently fragmented, everything now is customized, curated around a person’s profile or preferences based on various conditions
metrics
weight values on metrics according to person’s preferences
process
visualization & analysis criteria
target path : mid-manhattan
data-selection & mining
d1 : event density — twitter dataset
datamining through twitter api in spyder (python)
data cleaning through excel 2016
d2 : restaurant rating— yelp dataset 4.5~5.0 stars
datamining through yelp api in postman (oauth2.0 protocol)
data cleaning through excel 2016
d3 : building density — pluto dataset (16v1)
datamining through nyc planning dept. dataset (shape file)
d4 : 311 noise complaints dataset
datamining through nyc open data (csv for excel)
d5 : street trees dataset
datamining through nyc open data (csv for excel)
data-visualization & analysis
d1 : event density — how many tweets comes from on path
d2 : restaurant density — good restaurants for 4~5 stars from yelp
d3 : building density — built FAR / max. FAR from pluto data (16v1)
d4 : noise density — 311 complants data
d5 : trees density — street trees from nyc open data
s1 : visibility on path — view from street on walking
s2 : visibility on landmark — landmark view from street on walking
s3 : sky exposure — sky view from street on walking
short walk — distance from starting point to target point
final formula
optimal path algorithm concept
scripting & application into python editor in grasshopper
re-evaluation & synchronization between metrics and final formula
profiled — customized optimal path
conclusion
: this project allowing people possible to have control over
their optimal path that tell them where to go
