The SafeGraph Developer Hub

Welcome to the SafeGraph developer hub. You'll find comprehensive guides and documentation to help you start working with SafeGraph as quickly as possible, as well as support if you get stuck. Let's jump right in!

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How often is SafeGraph Places updated?

  • SafeGraph issues updates to Places once per month which is much more frequently than other POI vendors, who may update once every 3-6 months.
  • We can do this because we work with more sources of data, and are much more efficient at combining those sources of data. During each month, some subset of our sources will send us their updates, and we ensure that we onboard and integrate those changes quickly and easily.
  • This enables us to quickly reflect store openings and closings in our Places database.
    • The time between a store opening / closing and being reflected in our Places database is approximately equal to the time that that store update is seen by one of our sources + the time it takes SafeGraph to reflect this in our data.
    • The latter of these two is typically within the month -- which is very fast compared with competitors, which might be within 3 months.
    • However, the former of these two is hard to predict -- but we do work with sources that generally receive updates very quickly also.

How should I match SafeGraph Places with existing internal POI data?

  • Matching place data is very difficult. Some places will match immediately (i.e. store name, address, zipcode etc. are exactly the same), but the majority of places will not match. Is "peets coffee" at "345 5th street" in our database the same as "Peet’s Coffee & Tea" at "357 fifth st." in another database? Basic exact matching will not match these two, so your team will need to have built out advanced deduplication logic or else you will notice significant discrepancies.
  • SafeGraph offers a matching service that we recommend utilizing for this purpose. Please contact us if you're interested!

How do I work with the Patterns columns that contain JSON?

  • We have a simple web app for exploding the JSON here. You can explode it horizontally (into more columns) or vertically (into more rows). Just upload your file and pick which columns you want exploded. This is a quick and easy solution if you have a file with 1k or fewer rows (about 1MB) and do not want to explode beyond 20k rows.
  • If you ❤️Excel, we have an add-in that you can install to parse the JSON columns. The add-in can be downloaded here. See video demo of installation and usage. Written instructions are here. ⚠️This is only recommended for small samples of the data (100 rows or so)!
  • Want more control?
  • To horizontally explode the JSON into more columns programmatically, see an example using pandas here
  • To vertically explode the JSON into more rows programmatically, here are some code examples using python (pandas) or scala (click tabs):
This code takes SG Patterns data and vertically explodes 
the `visitor_home_cbgs` column into many rows. 
The resulting dataset has 3 columns: 
safegraph_place_id, visitor_count, visitor_home_cbg.

import pandas as pd
import json

patterns_df = pd.read_csv("safegraph_patterns_data.csv")

# convert jsons to dicts
patterns_df['visitor_home_cbgs_dict'] = [json.loads(cbg_json) for cbg_json in patterns_df.visitor_home_cbgs]

# extract each key:value inside each visitor_home_cbg dict (2 nested loops) 
all_sgpid_cbg_data = [] # each cbg data point will be one element in this list
for index, row in patterns_df.iterrows():
  this_sgpid_cbg_data = [ {'safegraph_place_id' : row['safegraph_place_id'], 'visitor_home_cbgs' : key, 'visitor_count' : value} for key,value in row['visitor_home_cbgs_dict'].items() ]
  # concat the lists
  all_sgpid_cbg_data = all_sgpid_cbg_data + this_sgpid_cbg_data

home_cbg_data_df = pd.DataFrame(all_sgpid_cbg_data)

# note: home_cbg_data_df has 3 columns: safegraph_place_id, visitor_count, visitor_home_cbg

# sort the result:
home_cbg_data_df = home_cbg_data_df.sort_values(by=['safegraph_place_id', 'visitor_count'], ascending = False)
import org.apache.spark.sql.functions._
import play.api.libs.json._

def parser(element: String) = {
  Json.parse(element).as[Map[String, Int]]

val jsonudf = udf(parser _)
val converted = df.withColumn("parsed_related_same_day_brand", jsonudf($"related_same_day_brand"))
display($"safegraph_place_id", explode($"parsed_related_same_day_brand" as "exploded_related_same_day_brand")))
val visitor_home_cbgs_parsed = df.withColumn("parsed_visitor_home_cbgs", jsonudf($"visitor_home_cbgs"))
display($"safegraph_place_id", explode($"parsed_visitor_home_cbgs" as "exploded_visitor_home_cbgs")))
  • If new to Spark (or prefer Python), check out this quick intro to Spark which includes an example of exploding JSON columns using PySpark.

How do I use SafeGraph Places in ESRI?

First, friendly reminder that Patterns does not have any geospatial data on its own. If you want to do geospatial analysis you should augment these datasets with Core Places which contains a latitude and longitude coordinate for every POI.

Let's say your goal is to visualize a point for each POI on a map and have the Patterns data available in the pop-up in ArcGIS Online (AGOL).

You have a few options for how to bring the data into AGOL.

1st, you can have ESRI geocode the POI for you by address. When you upload the Patterns data (as an unzipped csv) on the upload screen select "Locate by Address or Places" and select the appropriate columns. location_name > "place or address". and street_address > "place or address". city > city. state > state. At large scale (many rows) ESRI will charge you for this, but for small numbers of POI it should be trivial. The resulting feature service will show the Patterns data as points on a map as you would expect.

2nd, alternatively, you can use SafeGraph geospatial data. This is probably more accurate than having ESRI geocode for you, but it may not be worth the effort depending on your needs.

  • First load the CORE csv into AGOL. Instead of "Locate by Address or Places" select "Coordinates" and make sure latitude and longitude are mapped correctly (it should auto-detect this). * This should load successfully.
  • Then you load the Patterns data as a table (Locate Feature By > "None, add as table".
  • Open the CORE data in a map in AGOL and ADD the Patterns Table to the map.
  • Join on the Patterns table to the CORE layer using Analysis > Summarize > Join Features. The shared key is the safegraph_place_id, so you would join on this key one-to-one.
  • The resulting layer has both the geospatial data and the patterns data.

3rd, alternatively, you could join CORE csv and PATTERNS csv yourself in excel BEFORE loading into AGOL, joining on safegraph_place_id. e.g. via VLOOKUP(). Then just load this file as described in Step 1 for the CORE file above.

4th, FYI SafeGraph has made available all of our core places data (plus latitude and longitude centroids) for free via the ESRI Marketplace. This workflow would be similar to the 2nd workflow described above, except instead of loading the CORE file you would just use the feature layer from the marketplace listing. Then you could join the patterns data table onto that feature layer joining on safegraph_place_id one-to-one.

How does SafeGraph assign NAICS code to points-of-interest?

  • We strive to assign each point-of-interest the most reasonable, sensical and appropriate NAICS code. We have a multi-prong approach. We have used human-experts to label NAICS to brands. We use the business name as an indication of its category. We have built flexible mappings from many other category systems into NAICS, so that we can leverage category information from other sources. We have also crawled extra open-source information about a point-of-interest to infer the most correct NAICS code.
  • Note that most data that SafeGraph curates and reports have objective truth, like zip_code or visits_by_day. In contrast, there is no objective truth for NAICS code. NAICS are detailed descriptive categories created by governments but they do not perfectly describe every business. There are many examples of a point-of-interest that reasonably fits in to multiple NAICS or does not fit in to any NAICS very well. In these cases we strive for the "most correct" answer.
  • If you see a NAICS code that doesn't make sense to you -- let us know!

BigQuery does not like my polygons?

  • We have found that running the ST_GEOGFROMTEXT function in Google's BigQuery on our full dataset will return an error-- ST_GeogFromText failed: Invalid polygon loop. This is caused by only a handful of our polygons (under 20) not playing well with BigQuery. We have not encountered this issue with other geo libraries.
  • So that this does not stop you generally from calling this function on the polygons, use SAFE.ST_GEOGFROMTEXT(wkt). This will result in your function running and the few problematic polygons will just return NULL.
  • We are looking into a solution so that this error does not occur at all.

What are you using for MSAs in the Data Bar?

  • You might have noticed that you can order data by Metropolitan Statistical Area in the Data Bar.
  • The MSAs are defined here.

Where does the device data used in Patterns come from?

We partner with mobile applications that obtain opt-in consent from its users to collect anonymous location data. From the data provided by these partners, we see about 35 million unique anonymous devices over the course of each month. This data is not associated with any name or email address. This data includes the latitude and longitude of a device at a given point in time. We take this latitude/longitude information and determine visits to points of interest. We then aggregate these anonymous visits to create our Patterns product.

Do you have historical Patterns data?

  • Yes! We have Patterns data going back to late 2016. The last 3 months are available in the Data Bar. Beyond that, please contact us.
  • Note that in order to successfully compare the data over time, you are going to want to normalize based on our panel size over time. Each monthly delivery of Patterns includes the Panel Overview Data to enable this normalization. Our Normalization White Paper provides some guidance on how to go about doing this.
  • Also, the underlying Places data used to create Patterns changes over time. Due to the history of how we built and updated the product, the Patterns activity from October 2016 through March 2019 is based on our April 2019 release of Places. Activity from April and May of 2019 is based on our May 2019 release of places. Activity from June 2019 onwards is based on the Places release of the same month (so June 2019 activity is using June 2019 Places release).


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