Forbesganj Consultancy Services

Research on Identifying Strategic locations for Advertising Emergency Response Services of Forbesganj Heart Research Institute (FHRI), Pune.

Client Profile

India’s leading super speciality centre for research and treatment of cardiovascular diseases.

Research Team Members

• 71610033 - Kunal Dharmadhikari

• 71610051 - Praveen K Orvakanti

• 71610086 - Sudarshan Singh

• 71610105 - Vikram Menon Malik

• 71610113 - Vipul Manocha

Decision Problem

Identifying key strategic locations to put up billboards to advertise FHRI’s emergency response numbers & services.

Research Objectives

To segment customers & identify potential business opportunities.

Why we picked Pune?

• Rapid development & urbanization of the city of Pune (2nd largest city in Maharashtra).

• Ever increasing costs & limited availability of land in Mumbai. List the proxy entities you are going to search for.

• Recently shortlisted under the smart city initiative with focus on driving social transformation, developing infrastructure & building on its strong business environment.

Client’s Target Segment

• Large MNC’s.

• Middle & Upper SEC.

• High Net-worth Individuals.

• Service businesses like Education, Health Care.

Entity List

• Restaurants

  • Restaurants represent presence of people & cluster of restaurants represent a high population density area. One can also assess the purchasing power of the target customer segment.

• Hospitals

  • Generally located in the centre of the city where population density is higher and in areas where middle & upper class SEC’s are residing.

• Airport

  • Serving large volume of passengers and situated away from the main city, the roads/approach leading to the airport are generally uncluttered and serve as hot spots for advertising.

Results

• Map Overview

• Clustered Proxies Map

• Hoarding 1 Location :-

• Hoarding 2 Location

• Hoarding 3 Location

• Hoarding 4 Location

Inference

• Near New Airport Road & Pune Nagpur Road

  • Intersection of the New Airport Road & Pune Nagpur Road where volume of commuters is high.

  • Wide roads & highways making placing billboards an attractive option as vehicles on road get glimpse of advertised billboard.

• Intersection of Koregaon Park Road & N Main Road

  • Located near German Bakery, Starbucks, Taj Vivanta & Country Club.

  • Representative of the effluent & HNI population in the city.

• Subhash Chandra Bose Chowk

  • Representative of the middle & upper SEC.

  • Restaurants, Hospitals and MNC’s like TCS nearby.

  • Population density is high with high footfall because of the location.

  • Located near the Mumbai highway, College of Engineering Pune & Railway station.

• Khanduji Baba Chowk

  • Footfall is high as location is major intersection connecting rest of Pune.

  • Central Pune, located between Deccan Gymkhana, Hospitals & Restaurants like Barbeque Nation.

  • Vehicular traffic which stops at the traffic signals raises probability of viewership of the advertised billboard.

R Program Code

1. Map Overview R-Program.

library("RCurl")
library("jsonlite")
library("plotGoogleMaps")
library("geosphere")

key = "AIzaSyAC9fRXK8HIZO5ISi4HRipr0Q7_5dj4tGU"

url = paste0("https://maps.googleapis.com/maps/api/place/radarsearch/json?&query=hospitals+in+pune&types=hospital&location=18.520430,73.856744&radius=15000&key=",key)
doc <- getURL(url, ssl.verifyhost = 0L, ssl.verifypeer = 0L)
x <- jsonlite::fromJSON(doc)
hospitals = x$results$geometry$location
head(hospitals)

url = paste0("https://maps.googleapis.com/maps/api/place/radarsearch/json?&query=restaurants+in+pune&types=restaurant&location=18.520430,73.856744&radius=15000&key=",key)
doc <- getURL(url, ssl.verifyhost = 0L, ssl.verifypeer = 0L)
x <- jsonlite::fromJSON(doc)
restaurants = x$results$geometry$location
head(restaurants)

url = paste0("https://maps.googleapis.com/maps/api/place/radarsearch/json?&query=airport+in+pune&types=airport&location=18.520430,73.856744&radius=15000&key=",key)
doc <- getURL(url, ssl.verifyhost = 0L, ssl.verifypeer = 0L)
x <- jsonlite::fromJSON(doc)
airports = x$results$geometry$location
head(airports)

hospitals$type = "Hospital"
restaurants$type = "Restaurant"
airports$type = "Airport" 

data = rbind(hospitals,restaurants,airports)
dim(data)
write.csv(data,"pune_places.csv", row.names = F)
sample = data

coordinates(sample) <-~ lng +lat # Create cordinates
proj4string(sample) = CRS('+proj=longlat +datum=WGS84') # Add Projections
m<-mcGoogleMaps(sample,zcol = "type", mapTypeId='ROADMAP',legend=TRUE) #Plot on Google maps

2. Distance Matrix R Program.

library("RCurl")
library("jsonlite")
library("plotGoogleMaps")
library("geosphere")
library("ggplot2")
library("plotGoogleMaps")

key = "AIzaSyAC9fRXK8HIZO5ISi4HRipr0Q7_5dj4tGU"

# hospitals
url = paste0("https://maps.googleapis.com/maps/api/place/radarsearch/json?&query=hospitals+in+pune&types=hospital&location=18.520430,73.856744&radius=15000&key=",key)
doc <- getURL(url, ssl.verifyhost = 0L, ssl.verifypeer = 0L)
x <- jsonlite::fromJSON(doc)
hospitals = x$results$geometry$location
head(hospitals)

# restaurants
url = paste0("https://maps.googleapis.com/maps/api/place/radarsearch/json?&query=restaurants+in+pune&types=restaurant&location=18.520430,73.856744&radius=15000&key=",key)
doc <- getURL(url, ssl.verifyhost = 0L, ssl.verifypeer = 0L)
x <- jsonlite::fromJSON(doc)
restaurants = x$results$geometry$location
head(restaurants)

# airport
url = paste0("https://maps.googleapis.com/maps/api/place/radarsearch/json?&query=airport+in+pune&types=airport&location=18.520430,73.856744&radius=15000&key=",key)
doc <- getURL(url, ssl.verifyhost = 0L, ssl.verifypeer = 0L)
x <- jsonlite::fromJSON(doc)
airports = x$results$geometry$location
head(airports)

hospitals$type = "Hospital"
restaurants$type = "Restaurant"
airports$type = "Airport" 

sample = hospitals
coordinates(sample) <-~ lng +lat # Create cordinates
proj4string(sample) = CRS('+proj=longlat +datum=WGS84') # Add Projections
# m<-mcGoogleMaps(sample,zcol = "type", mapTypeId='ROADMAP') # Plot on Google maps


# Get the coordinates
p2 = hospitals[,2:1]

# calculate distances
dist_mat = matrix(0,nrow(p2),nrow(p2))

for (i in 1:nrow(p2)){
  for (j in 1:nrow(p2)){
    dist_mat[i,j] = distCosine(p2[i,],p2[j,], r=6378173)/1000    
  }
}

class(dist_mat)
dist_mat[1:10,1:10]
max(dist_mat)

# Create clusters based in distances
fit <- hclust(as.dist(dist_mat), method="ward")
plot(fit) # display dendogram

groups <- cutree(fit, k=10) # cut tree into 10 clusters
# draw dendogram with red borders around the 10 clusters
rect.hclust(fit, k=10, border="red") 

sample = restaurants
coordinates(sample) <-~ lng +lat # Create cordinates
proj4string(sample) = CRS('+proj=longlat +datum=WGS84') # Add Projections
# m<-mcGoogleMaps(sample,zcol = "type", mapTypeId='ROADMAP') # Plot on Google maps


# Get the coordinates
p2 = restaurants[,2:1]

# calculate distances
dist_mat = matrix(0,nrow(p2),nrow(p2))

for (i in 1:nrow(p2)){
  for (j in 1:nrow(p2)){
    dist_mat[i,j] = distCosine(p2[i,],p2[j,], r=6378173)/1000    
  }
}

class(dist_mat)
dist_mat[1:10,1:10]
max(dist_mat)

# Create clusters based in distances
fit <- hclust(as.dist(dist_mat), method="ward")
plot(fit) # display dendogram

groups <- cutree(fit, k=10) # cut tree into 10 clusters
# draw dendogram with red borders around the 10 clusters
rect.hclust(fit, k=10, border="red") 

sample = airports
coordinates(sample) <-~ lng +lat # Create cordinates
proj4string(sample) = CRS('+proj=longlat +datum=WGS84') # Add Projections
# m<-mcGoogleMaps(sample,zcol = "type", mapTypeId='ROADMAP') # Plot on Google maps


# Get the coordinates
p2 = airports[,2:1]

# calculate distances
dist_mat = matrix(0,nrow(p2),nrow(p2))

for (i in 1:nrow(p2)){
  for (j in 1:nrow(p2)){
    dist_mat[i,j] = distCosine(p2[i,],p2[j,], r=6378173)/1000    
  }
}

class(dist_mat)
dist_mat[1:10,1:10]
max(dist_mat)

# Create clusters based in distances
fit <- hclust(as.dist(dist_mat), method="ward")
plot(fit) # display dendogram

groups <- cutree(fit, k=10) # cut tree into 10 clusters
# draw dendogram with red borders around the 10 clusters
rect.hclust(fit, k=10, border="red") 

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