Drawing Google Streetview images down an entire street using python

I’ve previously written about grabbing Google Streetview images given a particular address. For a different project I sampled images running along an entire street, so figured I would share that code. It is a bit more complicated though, because when you base it off an address you do not need to worry about drawing the same image twice. So I will walk through an example.

So first we will import the necessary libraries we are using, then will globally define your user key and the download folder you want to save the streetview images into.

#Upfront stuff you need
import urllib, os, json
key = "&key=" + "!!!!!!!!!!!!!YourAPIHere!!!!!!!!!!!!!!!!"
DownLoc = r'!!!!!!!!!!!YourFileLocationHere!!!!!!!!!!!!!!'  

Second are a few functions. The first, MetaParse, grabs the date (Month and Year) and pano_id from a particular street view image. Because if you submit just a slightly different set of lat-lon, google will just download the same image again. To prevent that, we do a sort of memoization, where we grab the meta-data first, stuff it in a global list PrevImage. Then if you have already downloaded that image once, the second GetStreetLL function will not download it again, as it checks the PrevImage list. If you are doing a ton of images you may limit the size of PrevImage to a certain amount, but it is no problem doing a few thousand images as is. (With a free account you can IIRC get 25,000 images in a day, but the meta-queries count against that as well.)

def MetaParse(MetaUrl):
    response = urllib.urlopen(MetaUrl)
    jsonRaw = response.read()
    jsonData = json.loads(jsonRaw)
    #return jsonData
    if jsonData['status'] == "OK":
        if 'date' in jsonData:
            return (jsonData['date'],jsonData['pano_id']) #sometimes it does not have a date!
            return (None,jsonData['pano_id'])
        return (None,None)

PrevImage = [] #Global list that has previous images sampled, memoization kindof        
def GetStreetLL(Lat,Lon,Head,File,SaveLoc):
    base = r"https://maps.googleapis.com/maps/api/streetview"
    size = r"?size=1200x800&fov=60&location="
    end = str(Lat) + "," + str(Lon) + "&heading=" + str(Head) + key
    MyUrl = base + mid + end
    fi = File + ".jpg"
    MetaUrl = base + r"/metadata" + size + end
    #print MyUrl, MetaUrl #can check out image in browser to adjust size, fov to needs
    met_lis = list(MetaParse(MetaUrl))                           #does not grab image if no date
    if (met_lis[1],Head) not in PrevImage and met_lis[0] is not None:   #PrevImage is global list
        urllib.urlretrieve(MyUrl, os.path.join(SaveLoc,fi))
        PrevImage.append((met_lis[1],Head)) #append new Pano ID to list of images
    return met_lis  

Now we are ready to download images running along an entire street. To get the necessary coordinates and header information I worked it out in a GIS. Using a street centerline file I regularly sampled along the streets. Based on those sample points then you can calculate a local trajectory of the street, and then based on that trajectory turn the camera how you want it. Most social science folks I imagine want it to look at the sidewalk, so then you will calculate 90 degrees to the orientation of the street.

Using trial and error I found that spacing the samples around 40 feet apart tended to get a new image. I have the pixel size and fov parameters to the streetview api hard set in the function, but you could easily amend the function to take those as arguments as well.

So next I have an example list of tuples with lat-lon’s and orientation. Then I just loop over those sample locations and draw the images. Here I also have another list image_list, that contains what I save the images too, as well as saves the pano-id and the date meta data.

DataList = [(40.7036043470179800,-74.0143908501053400,97.00),

image_list = [] #to stuff the resulting meta-data for images
ct = 0
for i in DataList:
    ct += 1
    fi = "Image_" + str(ct)
    temp = GetStreetLL(Lat=i[0],Lon=i[1],Head=i[2],File=fi,SaveLoc=DownLoc)
    if temp[2] is not None:

I have posted the entire python code snippet here. If you want to see the end result, you can check out the photo album. Below is one example image out of the 8 in that street segment, but when viewing the whole album you can see how it runs along the entire street.

Still one of the limitations of this is that there is no easy way to draw older images that I can tell — doing this approach you just get the most recent image. You need to know the pano-id to query older images. Preferably the meta data json should contain multiple entries, but that is not the case. Let me know if there is a way to amend this to grab older imagery or imagery over time. Here is a great example from Kyle Walker showing changes over time in Detroit.


Using Python to grab Google Street View imagery

I am at it again with using Google data. For a few projects I was interested in downloading street view imagery data. It has been used in criminal justice applications as a free source for second hand systematic social observation by having people code aspects of disorder from the imagery (instead of going in person) (Quinn et al., 2014), as estimates of the ambient walking around population (Yin et al., 2015), and examining criminogenic aspects of the built environment (Vandeviver, 2014).

I think it is just a cool source of data though to be honest. See for example Phil Cohen’s Family Inequality post in which he shows examples of auctioned houses in Detroit over time.

Using the Google Street View image API you can submit either a set of coordinates or an address and have the latest street view image returned locally. This ends up being abit simpler than my prior examples (such as the street distance API or the places API) because it just returns the image blob, no need to parse JSON.

Below is a simple example in python, using a set of addresses in Detroit that are part of a land bank. This function takes an address and a location to download the file, then saves the resulting jpeg to your folder of choice. I defaulted for the image to be 1200×800 pixels.

import urllib, os

myloc = r"C:\Users\andrew.wheeler\Dropbox\Public\ExampleStreetView" #replace with your own location
key = "&key=" + "" #got banned after ~100 requests with no key

def GetStreet(Add,SaveLoc):
  base = "https://maps.googleapis.com/maps/api/streetview?size=1200x800&location="
  MyUrl = base + urllib.quote_plus(Add) + key #added url encoding
  fi = Add + ".jpg"
  urllib.urlretrieve(MyUrl, os.path.join(SaveLoc,fi))

Tests = ["457 West Robinwood Street, Detroit, Michigan 48203",
         "1520 West Philadelphia, Detroit, Michigan 48206",
         "2292 Grand, Detroit, Michigan 48238",
         "15414 Wabash Street, Detroit, Michigan 48238",
         "15867 Log Cabin, Detroit, Michigan 48238",
         "3317 Cody Street, Detroit, Michigan 48212",
         "14214 Arlington Street, Detroit, Michigan 48212"]

for i in Tests:

Dropbox has a nice mosaic view for a folder of pictures, you can view all seven photos here. Here is the 457 West Robinwood Street picture:

In my tests my IP got banned after around 100 images, but you can get a verified google account which allows 25,000 image downloads per day. Unfortunately the automatic API only returns the most recent image – there is no way to return older imagery nor know the date-stamp of the current image. (You technically could download the historical data if you know the pano id for the image. I don’t see any way though to know the available pano id’s though.) Update — as of 2018 there is now a Date associated with the image, specifically a Year-Month, but no more specific than that. Not being able to figure out historical pano id’s is still a problem as far as I can tell as well.

But this is definitely easier for social scientists wishing to code images as opposed to going into the online maps. Hopefully the API gets extended to have dates and a second API to return info. on what image dates are available. I’m not sure if Mike Bader’s software app is actually in the works, but for computer scientists there is a potential overlap with social scientists to do feature extraction of various social characteristics, in addition to manual coding of the images.