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https://github.com/travishorn/poi-classifier


https://github.com/travishorn/poi-classifier

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# Geospatial Point of Interest (POI) Classifier



## Objective



A geospatial point of interest (POI) classifier that takes in data points with

latitude, longitude, and name information and predicts the category of each

point of interest. This project demonstrates data preprocessing, feature

engineering, and classification using geospatial data.



![A portion of a map showing national parks, monuments, etc. in the United

States](screenshots/map.png)



![Accuracy, precision, recall, and F1-score metrics for the

model](screenshots/scores.png)



Supported categories are: National Battlefield Site, National Battlefield,

National Heritage Site, National Historic Park, National Historic Reserve,

National Historic Trail, National Lakeshore, National Memorial, National

Military Park, National Monument, National Park, National Preserve, National

Recreation Area, National Recreational River, National River, National Scenic

River, National Scenic Trail, National Seashore, Other, Park, and Parkway.



## Data Source



National Parks, Monuments, Preserves, & Historical Parks from POI Factory:

http://www.poi-factory.com/node/31147



## Methods Applied



1. Data Collection: I gathered geospatial data points for different POIs. Each

   data point had latitude, longitude, and a name of the POI (e.g., Fort Dupont

   Park - 3600 F St SE Washington DC 20019>202-426-7723).

3. Data Preprocessing: I processed the data, handling missing values, removing

   duplicates, and performing any necessary data transformations. I added type

   information to categorize each point (e.g., National Park, National Heritage

   Site, etc.) for 80% of the data, reserving 20% for demonstration purposes.

4. Feature Engineering: I extracted relevant features from the geospatial data

   that could be useful for classification. In this case: latitude, longitude,

   and name, as they were the only data points available in the data.

5. Model Selection: I chose and implemented a random forest classifier using the

   popular machine learning library scikit-learn for Python.

6. Model Training: I split the data into training and testing sets and trained

   your classifier on the labeled data.

7. Model Evaluation: I evaluated the classifier's performance using appropriate

   metrics such as accuracy, precision, recall, and F1-score.

8. Real-world Usage: I used the trained model to classify the remaining 20% of

   data. The result is a complete set of national parks, monuments, preserves,

   and historical parks in the United States, classified with their type.

9. Data Visualization: I created a map of all POIs using Folium which colors

   each marker differently depending on the type. This includes the types from

   the predicted set.



## Prerequisites



- Python

- `scikit-learn`, `pandas`, and `folium` installed through `pip`

- If you want to use the pre-trained model on your own data: a CSV file

  containing latitude, longitude, and names of POIs

- If you want to train your own model: a CSV file containing latitude,

  longitude, name, and types of POIs

- If you want to use the same source data as I did: a copy of the data from [POI

  Factory](http://www.poi-factory.com/node/31147)



## Usage



Clone this repository



```sh

git clone https://github.com/travishorn/poi-classifier

```



Change into the directory



```sh

cd poi-classifier

```



### Train



If you want to train your own data, make sure the training set is a CSV file

named `poi_training.csv` with columns for `latitude`, `longitude`, `name`, and

`type`.



Train the model



```sh

python train.py

```



The script will reserve 20% of the training file for testing itself. Scores are

shown and `trained_model.pkl` is created (or overwritten).



![Accuracy, precision, recall, and F1-score metrics for the

model](screenshots/scores.png)



### Classify



The trained model saved at `trained_model.pkl` can be used to classify types of

POIs. Make sure the "to classify" set is a CSV file named `classify.csv` with

columns for `latitude`, `longitude`, and `name`.



Predict (classify) the POIs



```sh

python classify.py

```



A sample of the predicions are shown and `poi_classified.csv` is created which

should mirror `poi_to_classify.csv`, but with a `type` column.



![POIs along with their predicted types](screenshots/classification_results.png)



### Visualize



Generate a map of POIs using a combination of `poi_training.csv` and

`poi_classified.csv`, which should now include a complete set of national parks,

monuments, preserves, and historical parks in the United States. Each marker is

colored according to the POI's type (e.g., national scenic rivers are dark blue,

national memorials are beige, etc.).



![A portion of a map showing national parks, monuments, etc. in the United

States](screenshots/map.png)



## License



The MIT License



Copyright 2023 Travis Horn



Permission is hereby granted, free of charge, to any person obtaining a copy of

this software and associated documentation files (the “Software”), to deal in

the Software without restriction, including without limitation the rights to

use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of

the Software, and to permit persons to whom the Software is furnished to do so,

subject to the following conditions:



The above copyright notice and this permission notice shall be included in all

copies or substantial portions of the Software.



THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS

FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR

COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER

IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN

CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.