#Google API Assignment-2

Client: Kokilaben Hospital

Region: : Mumbai

Business: Multi-specialty Hospital Group

Group Name: Boston Tycoons

Group Members:

  • Abhishek Nigam : 71610001
  • Bhargab Dutta : 71610010
  • Hemant Sahni : 71610029
  • Navjeet Singh : 71610041

##Prologue

Inaugurated in 2009, Kokilaben Hospital is a significant social initiative from Reliance Group. It is designed to raise global standing as a health-care destination with a vision to strengthen health-care in the communities and empowering patients to make informed decisions.

Kokilaben Hospital wants to start a super specialty Cardiac Center in Mumbai. For which they want to know four best locations in Mumbai to put up their promotional hoardings about their upcoming establishment. Our job is to find these best FOUR locations using Google APIs basis on analytics of various entities within Google map. > Decision Problem : > > What are the FOUR best locations in Mumbai to put promotional > hoardings? > > Research Objective :

  1. Find the high population density residential areas and localities with upper or middle class income group.
  2. Identify the right entities which will lead to the attention of target population.

##Why Mumbai?

Below listed are the major reasons, which qualifies Mumbai city as an ideal place for establishing a super specialty Cardiac Center which will be beneficial both commercially and socially.

  • Mumbai has the highest number of people within high income group among all cities in India. Additionally, Mumbai’s average household income is significantly higher than majority of the Indian cities.
  • Unlike Delhi, Mumbai does not have many dedicated super specialty Cardiac Centers. Majority of them are under various multi-specialty hospital groups.
  • Mumbai has the best modes of transportation available, having the busiest airports and one of the largest railway networks in western part of India.

Entity List:

  • Shopping Malls
  • Banks
  • Hospitals
  • Schools

##Entity Clusters - Results

Shopping Malls

In a city like Mumbai, malls are popular hangout places for regular citizens which multi-folds in the weekends. Any locality which having higher concentration of malls will bring more crowd constituting upper and middle class populations

ShoppingMallshere

Thus, shopping Malls is a place where people usually with their families to relax and can act as a subconscious trigger for their health and medical awareness of their near and dear ones.

Banks

Mumbai being the financial capital of India, a large section of Mumbai’s population deals with financial transactions from ATM to other investment and banking related work.

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This high stressed people engaged in their busy day to day life would be easy to approach and target about a medical healthcare linked hospital and seek their attentions.

Hospitals

Existing hospitals will become a direct point of reference for patients coming for curing their ailments and other medical care treatments. As near hospitals there is high availability of means of commute, hence it would be easy to gather the attention of our target audience for any cardiac care facilities future requirements.

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Schools

In schools, parents usually come with devoted time for taking feedback of their wards or to discuss any concerns with the school authorities. They also make regular visits i.e. around twice a month for parent teachers meeting to take the status of their wards. Today’s approximately all the major school have grown up to international level and only a upper middle class or higher class can effort a good school for their wards. So, that would provide us a very good platform for targeting our preferred audience round the year.

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Study Results: Banner Locations

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Code Snippet:

> rm(list=ls(all=TRUE))
> library("RCurl",lib.loc="D:/workSpace/R_packages") 
> library("jsonlite", lib.loc="D:/workSpace/R_packages") 
> library("plotGoogleMaps", lib.loc="D:/workSpace/R_packages") 
> library("geosphere", lib.loc="D:/workSpace/R_packages")


> key = "AIzaSyDp8Cr_znShnUMassocHgqGmOTD7CCp0s4"
> 
> `#`malls search url_malls = paste0("https://maps.googleapis.com/maps/api/place/radarsearch/json?&query=malls+in+Mumbai&types=shopping_mall&location=19.0886,72.8681&radius=50000&key=",key)
> 
> doc_malls <- getURL(url_malls) 
> 
> mall_json_object <- jsonlite::fromJSON(doc_malls) 
> 
> malls_data = mall_json_object$result$geometry$location 
> 
> head(malls_data)
> 
> `#`hospitals search 
> url_hospitals = paste0("https://maps.googleapis.com/maps/api/place/radarsearch/json?&query=hospitals+in+Mumbai&types=hospital&location=19.0886,72.8681&radius=50000&key=",key)
> 
> doc_hospitals <- getURL(url_hospitals) 
> hospital_json_object <- jsonlite::fromJSON(doc_hospitals) 
> 
> hospitals_data =hospital_json_object$result$geometry$location 
> head(hospitals_data)
> 
> `#`bank search 
> url_banks = paste0("https://maps.googleapis.com/maps/api/place/radarsearch/json?&query=banks+in+Mumbai&types=bank&location=19.0886,72.8681&radius=50000&key=",key) 
> doc_banks <- getURL(url_banks) 
> bank_json_object <- jsonlite::fromJSON(doc_banks) 
> banks_data = bank_json_object$result$geometry$location 
> head(banks_data)
> 
> `#`places of school search 
> 
> url_school = paste0("https://maps.googleapis.com/maps/api/place/radarsearch/json?&query=schools+in+Mumbai&types=school&location=19.0886,72.8681&radius=50000&key=",key)
> 
> doc_school <- getURL(url_school) 
> 
> school_json_object <-jsonlite::fromJSON(doc_school) 
> school_data = school_json_object$result$geometry$location 
> 
> head(school_data)
> 
> malls_data$Places = "Mall" 
> banks_data$Places = "Bank"
> hospitals_data$Places = "Hospital" 
> school_data$Places = "School"
> 
> total_data = rbind(malls_data,banks_data,hospitals_data,school_data)
> > dim(total_data) 
> summary(total_data)
> 
> City_Malls = malls_data 
> coordinates(City_Malls) <- ~ lng + lat        
>`#`create coordinates 
>proj4string(City_Malls) = CRS('+proj=longlat +datum=WGS84')
>`#`Add projections 
>mall_map <- mcGoogleMaps(City_Malls, zcol="Places", mapTypeId='ROADMAP') 
>
>#Plot on google map
> 
> 
> City_Hospitals = hospitals_data 
> coordinates(City_Hospitals) <- ~ lng + lat 
> proj4string(City_Hospitals) = CRS('+proj=longlat +datum=WGS84')
> hospital_map <- mcGoogleMaps(City_Hospitals, zcol="Places", mapTypeId='ROADMAP')
> 
> 
> City_Banks = banks_data 
> coordinates(City_Banks) <- ~ lng + lat
> proj4string(City_Banks) = CRS('+proj=longlat +datum=WGS84') 
> bank_map <- mcGoogleMaps(City_Banks, zcol="Places", mapTypeId='ROADMAP')
> 
> 
> City_School = school_data 
> coordinates(City_School) <- ~ lng + lat
> proj4string(City_School) = CRS('+proj=longlat +datum=WGS84')
> school_map <- mcGoogleMaps(City_School, zcol="Places", mapTypeId='ROADMAP')
> 
> `#`lets plot all the malls, hospital and bank individually in google maps 
> City_Total_Places = total_data
> `#`create coordinates 
> coordinates(City_Total_Places) <- ~ lng + lat
> `#`Add projections 
> proj4string(City_Total_Places) = CRS('+proj=longlat +datum=WGS84') All_map <- mcGoogleMaps(City_Total_Places, zcol="Places", mapTypeId='ROADMAP', col=topo.colors(5)) 
> #Plot on google map
> `#` Plot final map with clustors of combined data (of Banks, Malls and Hospitals), to know the major target population areas for marketing
> hoarding placement
> `#` Get the coordinates, Rows are not known hence left blank, coloumn are 1-2. 
> 
> count = total_data[,1:2]
> `#` calculate distances 
> distance_matrix = matrix(0,nrow(count),nrow(count)) 
> for (i in 1:nrow(count)){   
> for (j in 1:nrow(count)){
>     distance_matrix[i,j] = distCosine(count[i,],count[j,], r=6378173)/1000       } } 
> class(distance_matrix)
> distance_matrix[1:10,1:10]
> `#` Create clusters based in distances
> tree_data <- hclust(as.dist(distance_matrix), method="ward.D") 
> plot(tree_data) 
> #display dendogram 
> groups <- cutree(tree_data, k=12) 
> # cut tree into 12 clusters
> `#` draw dendogram with red borders around the 12 clusters 
> rect.hclust(tree_data, k=12, border="red")  
> City_Total_Places$group = groups 
> # Assign cluster groups 
> proj4string(City_Total_Places) = CRS('+proj=longlat +datum=WGS84')
> `#` Plot final map with clustors of combined data (of Banks, Malls, Schools and Hospitals), to know the major target population areas for
> marketing hoarding placement 
> All_map_clustered <- mcGoogleMaps(City_Total_Places, mapTypeId='ROADMAP', zcol="group" ,col=topo.colors(12))