#Geo Map0 personne en discute

• We have 13,000 Leads with addresses
• SF Maps plots 3724 Leads
• SF Maps returns 1347 bad addresses
• 3724 + 1347 = 5071
• 5,071 - 13,000 = -7,929

So SF Maps isn't reading the majority of the Leads in our Org and I have no explanation as to why.

To test the validity of the addresses, I created a custom object with just lead names and addresses (I called it 'Map Points') and SF Maps plots all of these 13,000 records accurately.

So we know that:

• The Lead addresses are correct and accurate
• The Lead Addresses can be successfully mapped to lat/long and plotted by SF Maps
• There are no limitations to record count that are preventing these Leads from loading

What I've tried:

• Remove Skip Geocode Flag Batch
• Removing the lat/long and geocoding the Leads again
• Creating custom address fields and using those fields to plot

What's even more perplexing is that there are cases where the "Maps Nearby Map" component on the Record Page correctly plots a point, but the SF Maps app does not. This makes no sense, as they're using the exact same data and method. As far as I can tell, SF Maps is randomly choosing not to plot markers for certain records and there doesn't really seem to be a rhyme or reason as to why.

Does anyone have any idea what's going on?

#Salesforce Maps  #Mapping #Geo Map

Einstein Analytics GeoJSON format script

Hi All,

Ran into an issue reformatting a large GeoJSON file to upload into Einstein Analytics - the file size was far too large for EA to handle and the standard GeoJSON format doesn't allow you to map a field in your dataset to an area on the map. The trailhead (https://trailhead.salesforce.com/en/content/learn/projects/wave_charts_custom_map/wave_charts_custom_map_geojson) gives a quick and dirty script to do this but doesn't fully solve the formatting issues and the file size remains the same.

A friend and I created a python script to flatten the required Id field and reduce the file size by removing decimal places from the polygon coordinates and then remove duplicate values to drastically reduce the file size (albeit the polygons are slightly less accurate).

This script allowed me to format a 160mb file down to just over 30mb which Einstein can then handle and the map is accurate enough for my use case.

We have added the script to this github repo if anyone wants to use it.

https://github.com/neal-shah/sf-einstein-analytics-geojson-map-formatter

Let me know if you have any questions.

Saahil

#EinsteinAnalytics #GeoJSON #Geo Map #Python