Calculating weighted centroid location of the census pop blocks

[vibhore276]

Hi Everyone,

I am having trouble with implementing this idea. I am using Census Pop block data for my analysis. I have couple of stores around which I drew 3 miles of buffer and calculated the population density for that area. Now I also wanted to calculate the centroid location (lat/long) of the population in that region. For reference below is the conceptualized diagram.

Here the blue boxes are the population blocks which vary in sizes. Each size corresponds to amount of people live there. The dashed square box location is what I need to find out.

Please let me know in case anyone of you would need any clarifying questions. I'll be quick to provide my response

Thanks in advance

[DiganP]

@vibhore276 Do you have a list or number of people who live in that dotted square box? 

Are you able to attach a sample dataset that shows this?  

[LukeG · Accepted solution]

@vibhore276 Here is an article from way back in the day (2012).

http://www.chaosreignswithin.com/2012/01/how-to-generate-weighted-centroids-in.html

The customer used a weighted average of the latitude and longitude to reach the goal you are describing.

If you have X,Y coordinates, I'm sure you can do the same. The weighted average of the lat/long should represent the centroid location of the population.

[vibhore276]

Hi @DiganP 

Since I are restricted to share the original data, I just created a dummy version of the similar structure which I have with me.

The Store Information:

Store ID	Lat	Long
Store1 Store2 Store3	-75.51178 -75.564823 -75.532379	39.156687 39.153958 39.114079

The Population Block information:

Census Block ID	Pops	Lat	Long
Block 1 Block 2 Block 3 Block 4 Block 5 Block 6 Block 7 Block 8 Block 9 Block 10 Block 11 Block 12 Block 13 Block 14 Block 15 Block 16 Block 17 Block 18 Block 19 Block 20 Block 21 Block 22 Block 23	1002 30 93 209 1029 4567 92 12 5 6 98 345 782 99 56 2 1 400 8000 23 2 1 9	-75.546799 -75.546455  -75.527916-75.514526  -75.535469  -75.508003  -75.525513  -75.533066  -75.51796  -75.51178  -75.525856  -75.535812  -75.561905  -75.549202  -75.509033  -75.49633  -75.508347  -75.554695  -75.573921  -75.532722  -75.477791  -75.618896  -75.549545	39.160947 39.143641 39.136452 39.160947 39.165206 39.148966 39.155889 39.151895 39.146304 39.141245 39.170263 39.148434 39.154824 39.13512 39.128995 39.157752 39.174788 39.171062 39.17532 39.188893 39.136185 39.251929 39.294985

Let me know if this is helpful

[vibhore276]

Thanks a lot @LukeG