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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 to 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.

What version of census block groups does SafeGraph use for the Patterns products?

  • SafeGraph uses the 2010-2019 version of the census block groups for the U.S., specifically the 2016 vintage.
  • You can find more information and a link to download the U.S. census block group geometries on our Open Census Data page!
  • For Canadian entries in any cbg column (e.g., poi_cbg or visitor_home_cbgs), we use the Canadian Dissemination area designations (Canadian units have CA: as a prefix)

Which records were added, deleted, or changed since last release?

  • See the "Calculating Diffs" section of Data Science Resources for plug and play code to answer all of your burning diff questions.

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)!
  • We also have a SQL example if you use Snowflake. See Tutorial here.
  • 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 PySpark, Scala Spark, pandas, R, and SQL (click tabs):
This code takes SG Patterns data as a PySpark DataFrame 
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.

from pyspark.sql.functions import udf, explode
from pyspark.sql.types import *
import json

def parser(element):
  return json.loads(element, MapType(StringType(), IntegerType()))

jsonudf = udf(parser, MapType(StringType(), IntegerType()))

visitor_home_cbgs_parsed = df.withColumn("parsed_visitor_home_cbgs", jsonudf("visitor_home_cbgs"))
visitor_home_cbgs_exploded = visitor_home_cbgs_parsed.select("safegraph_place_id", explode("parsed_visitor_home_cbgs"))

display(visitor_home_cbgs_exploded.selectExpr("safegraph_place_id as safegraph_place_id", "key as visitor_home_cbg","value as visitor_count"))
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($"related_same_day_brand"))
display(converted.select($"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(visitor_home_cbgs_parsed.select($"safegraph_place_id", explode($"parsed_visitor_home_cbgs" as "exploded_visitor_home_cbgs")))
If you are working with large datasets (i.e. > 20,000 POI at a time), 
then you should consider the Python-Pyspark solution; 
it is much much more efficient). 

This code takes SG Patterns data as a pandas DataFrame 
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 = patterns_df.dropna(subset = ['visitor_home_cbgs'])
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)
# This code takes SG patterns data as a 
# data.frame (or, even better, a data.table)
# and vertically explodes the `visitor_home_cbgs`
# column into many rows, or the `visits_by_day` column.
# This results in one row for safegraph_place_id
# one for visitor_count
# and one for visitor_home_cbg/day

# if you don't have the SafeGraphR package:
# install.packages('remotes')
# remotes::install_github('SafeGraphInc/SafeGraphR')


# Generally, data.table::fread is preferred to read.csv
# but this is fine for small files
patterns_df <- read.csv('safegraph_patterns_data.csv')

# expand_cat_json expands categorical JSON variables like visitor_home_cbg

home_cbg_data_df <- expand_cat_json(patterns_df,
                                    expand = 'visitor_home_cbgs',
                                    index = 'origin_cbg',
                                    by = 'safegraph_place_id')
# Fix variable names
names(home_cbg_data_df)[names(home_cbg_data_df) == 'visitor_home_cbgs'] <- 'visitor_count'
names(home_cbg_data_df)[names(home_cbg_data_df) == 'origin_cbg'] <- 'visitor_home_cbgs'

# expand_int_json expands integer JSON variables like visits_by_day

day_data_df <- expand_int_json(patterns_df,
                               expand = 'visits_by_day',
                               index = 'day',
                               by = 'safegraph_place_id')
# Fix variable names
names(day_data_df)[names(home_cbg_data_df) == 'visits_by_day'] <- 'visitor_count'
This code explodes the popularity_by_hour column into rows.
The result will be one row per hour per POI (i.e., 24 x NUM_POIs rows).
The table name here will work for the Starbucks Free Sample data in
Snowflake. Replace with your own table name as desired.
Note also that we take the INDEX value from the expl table below because the
popularity_by_hour column has no keys. For other JSON columns with keys,
replace expl.INDEX with expl.KEY.

WITH exploded AS (
  ,expl.INDEX as hour
  ,expl.VALUE as visits
  TABLE(FLATTEN(input => PARSE_JSON(patterns.popularity_by_hour))) as expl
FROM exploded

How do I work with SafeGraph data in Spark?

val df = spark.read.option("header", "True")
  .option("escape", "\"")

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 Geometry, which contains a latitude and longitude coordinate for every POI.

Visualizing POI as point data in Esri

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 the 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.

Visualizing POI as polygons in Esri

There are a few methods to take the data in polygon_wkt in SafeGraph Geometry and visualize the data. Unfortunately, ArcGIS Online cannot natively read the polygon_wkt, so you will have to convert it.

This Google colab notebook illustrates a best practice for converting SafeGraph Geometry files to Esri SHP files using geopandas. :tada+:

Alternatively, if you are working with arcpy, you can convert a WKT to a ArcGIS Polygon Geometry using the fromwkt() function. If none of these are meeting your workflow needs, we recommend contacting Esri support to develop a workflow solution. See Also: Visualizing WKT.

How can I visualize the polygon_wkt from SafeGraph Geometry?

If you are proficient with Esri tools, then you have some options in Esri.
If you are not familiar with any GIS tools and just looking for some quick and easy visualizations, we recommend Kepler.gl. You can upload a SafeGraph CSV directly into Kepler and see points and polygons within seconds.

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 use human-experts to label NAICS to brands. We use the business name as an indication of its category. We have also crawled extra open-source information about a point-of-interest to infer the most correct NAICS code. We use a deep neural network model to match long tail POI to NAICS based on name and other data points we have crawled.
  • 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 into multiple NAICS or does not fit into 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 Shop?

  • You might have noticed that you can order data by Metropolitan Statistical Area in the SafeGraph Shop.
  • 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. 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 January 1st, 2018 for the US and back to January 1st, 2019 for Canada. Beyond that, please contact us.
  • In order to successfully compare the data over time, we encourage normalizing based on our panel size over time. Each monthly and weekly delivery of Patterns includes the Patterns to enable this normalization. Please see our Data Science Resources for guidance on how to go about doing this.
  • Please note that the underlying Places (i.e., Core + Geometry) data used to create Patterns changes over time. Ongoing releases will always be using the latest version of Places: for example, all Patterns data from Jan 2021 onward will encompass the new POIs added to Core in Jan 2021 (i.e., industrial POIs). This also means that historical Patterns data will not contain new POIs until historical Patterns are re-generated with new versions of Places, which is generally done no more than twice a year. See the Backfill Key Concept in Patterns for more details.
  • Below is a reverse-chronological breakdown of the Places releases used to backfill Patterns for past releases. This is provided for transparency only - once a new backfill is released, we advise to always use the latest re-generated version of historical Patterns (except in very rare situations), as that will be most consistent with ongoing releases (in terms of schema, POI, bug fixes, etc.):
    • Patterns provided/delivered July 2021 onward:
      --Activity from January 2018 through and including July 2021 was generated using the July 2021 release of Places.
      --Activity from August 2021 onwards will be based on the Places release of the same month as the activity (so August 2021 activity will use the August 2021 Places release).
    • Patterns provided/delivered between December 2020 and June 2021:
      --Activity from January 2018 through and including December 2020 was generated using the Dec 2020 release of Places. This was the first historical delivery that considers point-in-time POI openings/closures. For example, if a POI opened in January 2019, we will not attribute visits to the POI from January 2018 - December 2018 and will only attribute visits from January 2019 onward. On the other hand, if a POI closed in January 2019, we will only attribute visits from January 2018 - December 2018 and will not attribute visits from January 2019 - present.
      We are relying on the metadata provided by our closed_on, opened_on, tracking_closed_since, and tracking_opened_since columns to make these determinations. If we do not have open/close information for a POI, we will treat the POI as “open” for the duration of the backfill. See here for more about how we determine POI openings/closings.
      --Activity from January 2021 to June 2021 was based on the Places release of the same month as the activity.
    • Patterns provided/delivered between May 2020 and November 2020:
      --Activity from January 2018 through and including May 2020 was generated using the May 2020 release of Places.
      --Activity from May 2020 thru and including November 2020 was based on the Places release of the same month as the activity (so June 2020 activity will use the June 2020 Places release).
    • Patterns provided/delivered between November 2019 and April 2020:
      -- Activity from January 2017 through and including October 2019 was generated using the November 2019 release of Places.
      --Activity from November 2019 through and including April 2020 was based on the Places release of the same month as the activity (so December 2019 activity will use the December 2019 Places release).
    • Historical Patterns activity from October 2016 through and including December 2016 was generated using the April 2019 release of Places. We no longer externally provide this data.

In Weekly / Neighborhood Patterns, why do visits / stops show spikes at 8pm ET / 5pm PT?

Prior to generating visits from GPS pings, SafeGraph groups the raw GPS inputs by the day (in UTC time) on which they occurred. This has the effect of “splitting” visits that cross the UTC day boundary (7/8pm EST/EDT and 4/5pm PST/PDT). Specifically, the following columns are impacted:

  • raw_visit_counts and visits_by_day: Visits that begin prior to and continue after the UTC day boundary will be split into two visits. POIs that conduct most of their business in the evening hours in the US will show inflated visit counts compared to those that receive most of their foot traffic in the morning and afternoon.

  • median_dwell and bucketed_dwell_times: Because visits that traverse the UTC day boundary are split, dwell times will be shorter for POIs that primarily do business in the evening.

  • visits_by_each_hour/stops_by_each_hour (Weekly Patterns/Neighborhood Patterns): Visits will show spikes in the hour immediately after the UTC day boundary (e.g., 8-9pm EDT and 5-6pm PDT), as visits that began prior to the day boundary will be registered as a separate, standalone visit after the day boundary. Note that we provide popularity_by_each_hour in Neighborhood Patterns as a complementary column in that product which does not have this behavior.

  • popularity_by_hour (Monthly Patterns): Conversely, popularity will be slightly depressed in the hour immediately following the UTC day boundary, because the GPS pings received in that hour alone may not be enough to register as a visit.

  • Note that columns related to visitor counts will be unaffected, as these are tied to the unique device ID that originated the pings.

Updated 18 days ago


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