Using the Google Vision and Streetview API to Explore Hotspots

So previously I have shown how to automate the process of downloading google street view imagery (for individual addresses & running down a street). One interesting application is to then code those streetview images. There are many applications in criminology of coding these images for disorder. So Rob Sampson initially had the idea of ecometrics, in which he used systematic social observations via taking a video going down various streets to code physical disorder, such as garbage on the street (Raudenbush & Sampson, 1999). Others than leveraged Google streetview imagery to do those same audits instead of collecting their own footage (Bader et al., 2017).

Those are all someone looks at the images and a human says, there is XYZ in this photo and ABC in this photo. I was interested in testing out the Google Vision API to automate identifying parts of the images. So instead of a human manually reviewing, you build a score automatically. See for example work on identifying the percieved safety of streets (Naik et al., 2014).

Here I was motivated by some recent work of a colleague, Nate Connealy, in which he used this imagery to identify the differences in hot spots vs not hot spots (Connealy, 2020). Also I am pretty sure I saw George Mohler present on this at some ASC before I had the idea (it was similar to this paper, Khorshidi et al., 2019, not 100% sure it was the same one though). For an overview of crim applications using streetview and google maps, which also span CPTED type analyses, check out Vandeviver (2014).

So with Google’s automated vision API, if I submit this photo of a parking garage (this is actually the image I get if I submit the address Bad Address, Dallas, TX to the streetview API, so take in mind errors like that in my subsequent analysis).

You get back these labels, where the first item is the description and the second is the ‘score’ for whether the item is in the image:

('Architecture', 0.817379355430603),
('Floor', 0.7577666640281677),
('Room', 0.7444316148757935),
('Building', 0.7440816164016724),
('Parking', 0.7051371335983276),
('Ceiling', 0.6624311208724976),
('Flooring', 0.6004095673561096),
('Wood', 0.5958532094955444),
('House', 0.5928719639778137),
('Metal', 0.5114516019821167)

So I don’t tell Google what to look for, it just gives me back a ton of different labels depending on what it detects in the image. So what I do here is based on my hotspot work (Wheeler & Reuter, 2020), I grab a sample of 300 addresses inside my Dallas based hot spot areas, and 300 addresses outside of hot spots. (These addresses are based on crime data themselves, so similar to Nate’s work I only sample locations that at least have 1 crime).

So this isn’t a way to do predictions, but I think it is potentially interesting application of exploratory data analysis for hot spots or high crime areas.

Python Code Snippet

I am just going to paste the python code-snippet in its entirety.

Grabbing streetview images and detecting
labels using the google vision API

from import vision
import pandas as pd
import io
import os
import urllib
import time


add_dat = pd.read_csv('Sampled_Adds.csv')
add_dat['FullAdd'] = add_dat['IncidentAddress'] + ", DALLAS, TX"

# Code to download image based on address 

myloc = r"./Images" #replace with your own location
key = "&key=????YourKeyHere????" 

def GetStreet(Add,SaveLoc,Name):
  base = ""
  MyUrl = base + urllib.parse.quote_plus(Add) + key #added url encoding
  fi = Name + ".jpg"
  loc_tosav = os.path.join(SaveLoc,fi)
  urllib.request.urlretrieve(MyUrl, loc_tosav)

# Code to get the google vision API labels
# for the image

client = vision.ImageAnnotatorClient.from_service_account_json('Geo Dallas-b5543ff0bb6d.json')

def LabelImage(ImageLoc):
    # Loads the image into memory
    with, 'rb') as image_file:
        content =
    image = vision.types.Image(content=content)
    response = client.label_detection(image=image)
    labels = response.label_annotations
    res = []
    if response.error.message:
        print(f'Error for image {ImageLoc}')
        print(f'Error Message {response.error.message}')
        res.append( ('Error', 1.0 ) )
        res = []
        for l in labels:
            res.append( (l.description , l.score) )
    return res

#A random parking garage!
GetStreet('Bad Address, Dallas, TX',myloc,'Bad_Address')    
long_tup = []
for index, row in add_dat.iterrows():
    #Name of the image
    nm = str(index) + "_" + str(row['Inside'])
    #Download the image    
    #Get the labels
    labs = LabelImage(os.path.join(myloc,nm + '.jpg'))
    #Build the new data tuples
    for l in labs:
        long_dat = (index, nm +'.jpg', row['Inside'], row['FullAdd'], l[0], l[1])
    #Sleep for a second to not spam the servers
    print(f'Done with index {index}')

long_dat = pd.DataFrame(long_tup, 

To get this to work you need a few things. First, you need to enable both the Vision API and the Streetview API in your Google API console. The streetview API has a key you can get directly from the API console (as described in my prior posts). But the vision API is different, and you can download a json file with all the necessary info and feed it into the client call. Once that is all done, you have it set up to query both API’s to get the images and then get the labels. But this is quick and dirty, it does not check for errors in either.

Here is a screenshot of some of the images downloaded, you can see that the streetview API doesn’t fail when their is no image available, it just does a mostly blank gray screenshot.

Analyzing the Results

I am not above just piping the results into an Excel document and doing some quick pivot tables. (I like doing that when there are many categories I want to explore quickly.) So here is a pivot table of the sum of the scores across the 300 outside hotspot (column 0) and 300 inside (column 1) images. So you can see the label of property is in more than half of the images for each (since the score value is never above 1). But property is more common outside hot spots than it is inside hot spots.

Here are contrast coded sums, so these identify the different labels that are more common in either hotspots or outside of hotspots. So outside of hotspots trees and plants appear more common (see Kondo et al., 2017 and Kondo’s other work on the topic). Inside hotspots we have more cars & asphault for examples.

This is just a quick and dirty analysis though. I do not take into account here missing images. The Screenshot label shows missing images are more common inside hotspots. And here since I use the addresses sometimes it gives me a shot of the street instead of the view perpendicular to the street. (I am not 100% sure the best way to do it, if you geocode and then use the lat/lon, you may not have the right view of the property either depending on the geocoding engine, so maybe going with the address directly is better?)

Future Work

In terms of predictive applications, I think using the streetview imagery is not likely to improve crime forecasts, that it is really only worthwhile for EDA or theory testing. In terms of predictive analysis, I actually think using the satellite imagery has more potential (see Jay, 2020 for an example, although that isn’t predictive but causal analysis).

So prior work has used 311 calls for service to identify high disorder areas (Magee, 2020; O’Brien & Winship, 2017; Wheeler, 2018), so I wonder if you can specifically build an image detector to identify particular disorder aspects that are not redundant with 311 calls. And also perhaps scales directly relevant to CPTED. The Google Vision labels are a bit superficial to really use for many theory crim applications I am afraid, but is an interesting exploratory data analysis to check them out.