{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Ransaka Ravihara",
  "title_listed": "Geospatial Indexing and Scoring: Unlocking the Power of Location-Based Data Analysis",
  "keywords": "地理空间索引,H3,Python",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "部分免费/会员墙",
  "url": "https://towardsdatascience.com/geospatial-indexing-and-scoring-unlocking-the-power-of-location-based-data-analysis-b77a7599b4c/",
  "publish_date": "2023-01",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "1d92f3b349ae",
  "collected_at": "2026-10-01T23:14:46",
  "title": "Geospatial Indexing and Scoring: Unlocking the Power of Location-Based Data Analysis | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Geospatial Indexing and Scoring: Unlocking the Power of Location-Based Data Analysis\nA hands-on guide on geospatial indexing using Python & H3\n[Ransaka Ravihara](https://towardsdatascience.com/author/ransakaravihara/)\nJanuary 25, 20236 min read\n![Photo by Antoine Merour on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/09uVzh9jAw7JqJNkP-scaled.jpg)\nPhoto by [Antoine Merour](https://unsplash.com/@amerour?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nIn this article, we will discuss geospatial indexing and scoring and how it can be made easy for developers. Geospatial indexing is the process of creating an index on a set of data that has a geographic location associated with it, such as latitude and longitude. Scoring, on the other hand, is the process of ranking or ordering the data based on specific criteria. Together, geospatial indexing and scoring can create powerful and efficient applications that can quickly retrieve and display geographic data.\nGeospatial indexing and scoring can be complex and time-consuming, but with the right tools and techniques, it can be made simple. This article will explore how we can use Uber's H3 library for indexing and scoring tasks.\nFor a better understanding, let's define the hypothetical problem statement.\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\n> As a Data Scientist at a retail company, you have been tasked with a critical project. The company needs to organize and understand the different regions it operates.\n> The end goal of this project is to split regions into different pieces and identify critical hotspots across each region. This information will be helpful for the company to understand which areas are most important for their business and where they should focus their efforts.\n> But your job doesn't end there. The company also needs you to assign an \"importance score\" to each of its outlets. This score will be based on the previously mentioned hotspot mechanism. The outlets that are located near potential hotspots have a higher importance score.\nAlright, let's start building this solution.\n### Steps to follow\n– [Know your data (Understand the data)](https://towardsdatascience.com/geospatial-indexing-and-scoring-unlocking-the-power-of-location-based-data-analysis-b77a7599b4c/#9242) – [Splitting region into small hexagons](https://towardsdatascience.com/geospatial-indexing-and-scoring-unlocking-the-power-of-location-based-data-analysis-b77a7599b4c/#a839) – [Derive a score for each hexagon in the region](https://towardsdatascience.com/geospatial-indexing-and-scoring-unlocking-the-power-of-location-based-data-analysis-b77a7599b4c/#add6) – [Define outlet importance score](https://towardsdatascience.com/geospatial-indexing-and-scoring-unlocking-the-power-of-location-based-data-analysis-b77a7599b4c/#2edc)\n### 1️⃣ Know your data\nBased on the problem statement, we have two datasets.\n  1. Points of interest dataset - To derive importance scores\n  2. Outlet location dataset\n\n\n_Note: The data provided is randomly generated and does not reflect real-world scenarios. You can access this data from_[ _here_](https://github.com/Ransaka/Spatial-indexing-with-h3/tree/master/data) _._\npython\n\n```\nimport pandas as pd\n\npois = pd.read_csv(\"pois.csv\")\noutlets = pd.read_csv(\"outlets.csv\")\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1AqoiZDvgRBsnDk_19kO-EQ.png)\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1yC7MHmXbYIxIbG4qPMje-Q.png)\nLet's plot the POI category distribution.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1jG0AZl3npdYZA4Uy4YZmgw.png)\nAdditionally, we will require regions' geographical information. I already created the regions' JSON file via the _geojson.io_ website.\nlua\n\n```\n{'type': 'FeatureCollection',\n 'features': [{'type': 'Feature',\n   'properties': {},\n   'geometry': {'coordinates': [[[80.19371259270753, 6.307037132469091],\n      [80.19371259270753, 6.072771744744614],\n      [80.4434199500783, 6.072771744744614],\n      [80.4434199500783, 6.307037132469091],\n      [80.19371259270753, 6.307037132469091]]],\n    'type': 'Polygon'}}]}\n```\n\nLet's put it all together.\n![What we have so far | Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1ev1ZfyijaC3qSbzhCMZqSg.jpeg)\nWhat we have so far | Image by Author\nIn the above diagram, you can see two layers.\n  1. Rectangular geojson layer - Represents the region we are analyzing\n  2. Points of interest layer - Provides points of interest dataset for the region\n\n\n### 2️⃣ Splitting region into small hexagons\nTo pinpoint hotspots across a specific area, it's necessary to divide the region into smaller sections. One way to do this is by using hexagons. Hexagons are particularly useful in this context because of the advantages they offer, such as their ability to evenly divide an area and the ability to accurately represent geographical shapes. If you have any further questions about why hexagons are used or the benefits they offer, you can refer to the official documentation of H3 for more information.\nIn simpler terms, by choosing the correct resolution, multiple locations can be represented by a single hexagon cell.\n![Map multiple points to one hexagon | Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1B4_U6j0RiN_6nbpMalUxhg.jpeg)\nMap multiple points to one hexagon | Image by Author\n> [**H3 | H3**](https://h3geo.org/)\nWe can use H3's `_polyfill_`function to split a given region into hexagons. But before that, we have to convert the region geojson file into a shapely polygon format.\ncss\n\n```\nfrom h3 import h3\nfrom shapely.geometry import Polygon,mapping\n\ndef dict_to_shapely(d):\n    coords = d['features'][0]['geometry']['coordinates'][0]\n    return Polygon(coords)\n\ndef polygon_to_h3(polygon,resolution):\n    polygon = mapping(polygon)\n    hexas = h3.polyfill(polygon,res=resolution,geo_json_conformant=True)\n    return list(set(hexas))\n\n#read geojson file\nwith open(\"sample-bbx.geojson\",'r') as f:\n    polygon_dict = json.loads(f.read())\n    shapely_polygon = dict_to_shapely(polygon_dict)\n    f.close()\n\nh3_idx = polygon_to_h3(shapely_polygon,resolution=8)\nh3_idx = pd.DataFrame(pd.Series(h3_idx,name='h3'))\n```\n\nIn the above snippet, the `polygon_to_h3` function will convert the input polygon into a list of hexagons. Also, you have to provide the resolution parameter. The resolution parameter defines the size of the hexagon. Based on the provided resolution parameter, we can split the given region into hexagons, like below.\n![Converting geojson tile into a list of hexagons | Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1Cm5lB-WG5wmVwsJBmvG1QQ.jpeg)\nConverting geojson tile into a list of hexagons | Image by Author\nIn this instance, a resolution of 8 was utilized. According to the documentation, hexagons with a solution of 8 have an area of 0.737327598 square kilometers. In this specific case, the region was divided into 912 hexagons.\n### 3️⃣ Derive a score for each hexagon in the region\nSince we have split regions into hexagons, we can now work on deriving the scores for each hexagon.\nThe methodology for this process includes the following:\n  * Assigning a unique hexagon ID to each point of interest in the dataset.\n  * Establishing the scoring criteria, with weighting given to each category.\n  * Joining the scored dataset with the region dataset.\n\n\npython\n\n```\nscores = 1- (pois['category'].value_counts(normalize=True)) * 3\nscores_dict = dict(zip(scores.index,scores.values * 100))\n\npois['weight'] = pois['category'].map(scores_dict)\n\ngrouped_df = pois.groupby(\"h3\").agg(\n    counts=('category','count'),\n    score = ('weight','sum')\n).sort_values(by='counts',ascending=False)\n\ngrouped_df.head()\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1CXNJ8HlvJpICAaRaX05ENg.png)\nLet's overlay the scoring layer on top of the base region hexagons. The figure on the right illustrates how scores are assigned to each hexagon. The height of the bar reflects the importance score.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1ZvxtkNMGgQxNgX9TwhRE1w.jpeg)\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1oQIHNZXHJNJ6j9ZO-M_D7w.jpeg)\nImage by Author\nLet's check how derived scores are distributed.\npython\n\n```\ngrouped_df.reset_index(inplace=True)\nfull_scored_idx = h3_idx.merge(grouped_df,on='h3',how='left').fillna(0)\n\nfull_scored_idx['score'].plot.hist(color='skyblue',edgecolor='black',bins=5,figsize=(10,8))\nplt.title(\"Score Distibutin\")\nplt.xlabel(\"Score\")\nplt.ylabel(\"Frequency\")\nplt.grid(visible=True, linestyle='-.')\nplt.show()\n```\n\n![Score distribution | Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1gNYP1vsc-xIS-RMuB8kXpg.png)\nScore distribution | Image by Author\nWith all of the hexagons in the region now scored, it is time to create a new data layer for visualization.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1e9ngM3SwpQmMU9s-7HeDdA.jpeg)\n![Region's hotspots | Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1RDBOuQR_zy13e6PrBjU-Yg.jpeg)\nRegion's hotspots | Image by Author\nPlease note the color code above, greyish-blue colored hexagons represent low-importance hexagons, while pink and cream-colored hexagons represent relatively important hexagons within the region.\n### 4️⃣ Define outlet importance score\nWe now have a dataset containing scores for all the hexagons in the selected region. This allows us to identify the area's most critical (hotspot) hexagons. This solves half of the problem. Next, we need to define the importance scores for the outlets.\nThe steps that will be followed include the following:\n  1. Assigning unique hexagon IDs to the outlet dataset.\n  2. For each outlet, identify the closest _K_ hexagons.\n  3. Defining the importance of the outlet based on scores of the _K_ hexagons.\n\n\npython\n\n```\nh3_to_score = dict(zip(full_scored_idx['h3'],full_scored_idx['score']))\nget_krings = lambda h:h3.k_ring(h,k=3)\noutlets_h3['rings'] = outlets_h3['h3'].apply(get_krings)\n\nprint(outlets_h3[['category','h3','rings']])\n```\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1Mb1zBFbwdQcTWfsAu5MKBg.png)\nIn the code snippet above, the `k_ring` function is used to retrieve the nearest K hexagons for each outlet. You will also notice that in this case, K has been set to 3.\nLet's visualize the data to understand better what we have accomplished in this step.\n![Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/13KeHwu9ourA6yBPn8KVITA.jpeg)\nImage by Author\nNow we are almost done. Because we have defined the scores for each hexagon cell in this region, all we need to do is aggregate the nearby hexagon scores for each outlet.\npython\n\n```\noutlets_exploded = outlets_h3.explode(column='rings')\noutlets_exploded['weight'] = outlets_exploded['rings'].map(h3_to_score)\n\n#can contains hexagons not relavent to current region\noutlets_exploded.dropna(inplace=True)\n\n#final scoring for outlets\noutlets_exploded.groupby(\"category\").agg(outlet_importance=(\"weight\",'sum'))\n```\n\n![Final Scoring | Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1ZANv54FgxKtnEiT3QnyAvw.png)\nFinal Scoring | Image by Author\nGreat, we have now successfully established the importance score for each outlet. With that, both objectives have been accomplished. Well done! :)\nYou can access all codes below the GitHub repo.\n> [**GitHub - Ransaka/Spatial-indexing-with-h3**](https://github.com/Ransaka/Spatial-indexing-with-h3.git)\n### **Conclusion**\nThis simplified example highlights some of the valuable capabilities of the H3 library. From measuring the distance between two geographic locations to performing nearest neighbor analysis, the H3 library offers a wide range of options. By incorporating spatial indexing at the appropriate time, not only can resources be saved, but costs can also be reduced. The reason is that performing mathematical operations on point data can consume a significant amount of memory, but memory usage can be greatly minimized with indexing.\nThanks for reading! Connect with me on [LinkedIn](https://www.linkedin.com/in/ransaka/).\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657265628_qm714e.jpeg)\nWritten By\n## [Ransaka Ravihara](https://towardsdatascience.com/author/ransakaravihara/)\n[See all from Ransaka Ravihara](https://towardsdatascience.com/author/ransakaravihara/)\n[Data Science](https://towardsdatascience.com/tag/data-science/)[Python](https://towardsdatascience.com/tag/python/)[Spatial Analysis](https://towardsdatascience.com/tag/spatial-analysis/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fgeospatial-indexing-and-scoring-unlocking-the-power-of-location-based-data-analysis-b77a7599b4c%2F&title=Geospatial%20Indexing%20and%20Scoring%3A%20Unlocking%20the%20Power%20of%20Location-Based%20Data%20Analysis)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fgeospatial-indexing-and-scoring-unlocking-the-power-of-location-based-data-analysis-b77a7599b4c%2F&title=Geospatial%20Indexing%20and%20Scoring%3A%20Unlocking%20the%20Power%20of%20Location-Based%20Data%20Analysis)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fgeospatial-indexing-and-scoring-unlocking-the-power-of-location-based-data-analysis-b77a7599b4c%2F&text=Geospatial%20Indexing%20and%20Scoring%3A%20Unlocking%20the%20Power%20of%20Location-Based%20Data%20Analysis)\n\n\nTowards Data Science is a community publication. 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