> ## Content Index > Fetch the complete content index at: https://serpapi.com/blog/llms.txt > Use this file to discover other available public pages before exploring further. # Gauge Business Popularities using Google Maps - URL: https://serpapi.com/blog/gauge-business-popularities-using-google-maps/ - Published: 2025-01-27T03:35:29.000Z - Updated: 2025-01-29T23:48:16.000Z - Author: Terry Tan - Tags: Google Maps A fun experiment with Google Maps data is to compare businesses’ popularity. Whether you’re a business seeking new opportunities or a visitor planning your trip, this analysis can help you make better decisions. To get the popularity data, we use [SerpApi’s Google Maps Scraper API](https://serpapi.com/google-maps-api). It’s one of our customers’ favorites for retrieving details like business addresses, phone numbers, and websites. We wrote another [blog post](https://serpapi.com/blog/google-maps-scraper/) that goes more in-depth about it. First of all, we use [Google Maps Scraper API](https://serpapi.com/google-maps-api) to search for a list of businesses (e.g. Museum, restaurant, coffee). Below is an example in Playground. We perform a search for `Museum` in New York using the `q` and `ll` parameter respectively. ![](https://storage.ghost.io/c/a5/00/a5004977-0dd2-4bcd-9292-dd0e05d4c59e/content/images/2025/01/Screenshot-2025-01-27-at-8.59.26-AM.png) SerpApi Google Maps API Playground Popularity data isn't available for listing searches; it is only available in [Place Results](https://serpapi.com/maps-place-results). To retrieve Place Results, we have to provide the `place_id`. Below is an example in Playground. ![](https://storage.ghost.io/c/a5/00/a5004977-0dd2-4bcd-9292-dd0e05d4c59e/content/images/2025/01/Screenshot-2025-01-27-at-9.02.47-AM.png) SerpApi Google Maps API Playground The data we need is `popular_times`. ```json "popular_times": { "graph_results": { "sunday": [ { "time": "6 AM", "busyness_score": 0 }, { "time": "7 AM", "busyness_score": 0 }, { "time": "8 AM", "busyness_score": 0 }, { "time": "9 AM", "busyness_score": 0 }, { "time": "10 AM", "info": "Usually not too busy", "busyness_score": 21 }, { "time": "11 AM", "info": "Usually not too busy", "busyness_score": 35 }, { "time": "12 PM", "info": "Usually not too busy", "busyness_score": 49 }, { "time": "1 PM", "info": "Usually a little busy", "busyness_score": 64 }, { "time": "2 PM", "info": "Usually a little busy", "busyness_score": 71 }, { "time": "3 PM", "info": "Usually a little busy", "busyness_score": 75 }, { "time": "4 PM", "info": "Usually a little busy", "busyness_score": 70 }, { "time": "5 PM", "info": "Usually a little busy", "busyness_score": 63 }, { "time": "6 PM", "info": "Usually a little busy", "busyness_score": 51 }, { "time": "7 PM", "info": "Usually not too busy", "busyness_score": 41 }, { "time": "8 PM", "info": "Usually not too busy", "busyness_score": 29 }, { "time": "9 PM", "info": "Usually not too busy", "busyness_score": 21 }, { "time": "10 PM", "info": "Usually not busy", "busyness_score": 15 }, { "time": "11 PM", "busyness_score": 0 } ], ... }, ... } ``` ## Doing it in code We will use this data to create a graph that better visualizes the popularity of businesses. A heatmap graph is excellent for this as it provides a clear visual representation of the varying levels of popularity across different businesses. By using a heatmap, we can easily identify trends, peak times, and the most popular businesses at a glance. ![](https://storage.ghost.io/c/a5/00/a5004977-0dd2-4bcd-9292-dd0e05d4c59e/content/images/2025/01/Screenshot-2025-01-27-at-9.26.06-AM.png) Business popularity heatmap graph In the `find_businesses_and_popularities` method, the process follows a similar flow to what we previously explained. It first retrieves the listings based on the search query and the coordinates provided. Then, it iterates through each item in the list to fetch the details of each business. \*API Key can be retrieved from the [dashboard](https://serpapi.com/manage-api-key). ```python from serpapi import SerpApiClient SerpApiClient.SERP_API_KEY = "" def find_businesses_and_popularities(query, ll): search_results = SerpApiClient( params_dict={"q": query, "ll": ll}, engine="google_maps" ).get_dict()["local_results"] tasks = [] for result in search_results: task = SerpApiClient( params_dict={"place_id": result["place_id"]}, engine="google_maps" ).get_dict() tasks.append((result, task)) for result, place_data in tasks: result["popular_times"] = place_data["place_results"].get("popular_times") return search_results local_results = find_businesses_and_popularities("Museum", "@40.7455096,-74.0083012,14z") ``` The data is not yet in a usable format; we need to flatten it into the following structure: ```json [ {"business": "...", "time": "6 AM", "busyness_score": 41}, {"business": "...", "time": "7 AM", "busyness_score": 59}, {"business": "...", "time": "8 AM", "busyness_score": 53}, ... ] ``` ```python data = [] for result in local_results: if result["popular_times"] is not None: for time_results in result["popular_times"]["graph_results"].values(): for time_result in time_results: data.append({ "business": result["title"], "time": time_result["time"].replace("\u202f", " "), "busyness_score": time_result["busyness_score"], }) ``` It is time to plot the graph! ```python import seaborn as sns import matplotlib.pyplot as plt import pandas as pd # Make sure we can display unicode character plt.rcParams['font.sans-serif'] = ['Arial Unicode MS'] plt.rcParams['axes.unicode_minus'] = False columns = ["6 AM", "7 AM", "8 AM", "9 AM", "10 AM", "11 AM", "12 PM", "1 PM", "2 PM", "3 PM", "4 PM", "5 PM", "6 PM", "7 PM", "8 PM", "9 PM", "10 PM", "11 PM", "12 AM", "1 AM", "2 AM", "3 AM", "4 AM", "5 AM"] df = pd.DataFrame(data) heat_data = df.pivot_table( index='business', columns='time', values='busyness_score', aggfunc='mean' ).reindex(columns=columns) plt.figure(figsize=(len(columns), 4)) sns.heatmap(heat_data, cmap='YlOrRd', annot=True, fmt='.0f') plt.title('Popularity by Hour') plt.xlabel('Hour of Day') plt.ylabel('Business') plt.show() ``` This graph represents data for the entire week. We use `mean` calculations to obtain an accurate overview. Feel free to modify the code to view them on a specific day or on the weekend. ### Museum in New York ![](https://storage.ghost.io/c/a5/00/a5004977-0dd2-4bcd-9292-dd0e05d4c59e/content/images/2025/01/Screenshot-2025-01-27-at-9.26.06-AM-1.png) Museum popularity heatmap graph ### Coffee shop in New York ![](https://storage.ghost.io/c/a5/00/a5004977-0dd2-4bcd-9292-dd0e05d4c59e/content/images/2025/01/Screenshot-2025-01-27-at-10.02.13-AM.png) Coffee shop popularity heatmap graph ### Chinese Restaurant in New York ![](https://storage.ghost.io/c/a5/00/a5004977-0dd2-4bcd-9292-dd0e05d4c59e/content/images/2025/01/Screenshot-2025-01-27-at-10.05.30-AM.png) Chinese restaurant in New York ### Korean Restaurant in New York ![](https://storage.ghost.io/c/a5/00/a5004977-0dd2-4bcd-9292-dd0e05d4c59e/content/images/2025/01/Screenshot-2025-01-27-at-10.12.45-AM.png) Korean Restaurant in New York ## Conclusion It's interesting to see some businesses peak at a certain period (lunch, dinner time) and some businesses peak almost the entire opening period. There are other interesting data points to be used in Google Maps, feel free to check them out! ## References - [Google Maps API](https://serpapi.com/google-maps-api)