geo-sampling 0.2.2
Scripts for sampling Geo data sets by the specific region name
Requires Python: >=3.10
Keywords: geo, street, road, openstreetmap, osm, city

# Geo sampling: Randomly sample locations on streets

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Say you want to learn about the average number of potholes per kilometer of street in a city. Or estimate a similar such quantity. To estimate the quantity, you need to sample locations on the streets. This package helps you sample those locations. In particular, the package implements the following sampling strategy:

## Sampling Strategy

### 1. Sampling Frame
Get all the streets in the region of interest from [OpenStreetMap](https://www.openstreetmap.org/#map=5/51.500/-0.100). To accomplish that, the package first downloads administrative boundary data for the country in which the region is located in ESRI format from http://www.gadm.org/country. The administrative data is in multiple levels, for instance, cities are nested in states, which are nested in countries. The user can choose a city or state, but not a portion of a city. And then the package uses the [pyshp package](https://pypi.python.org/pypi/pyshp) to build a URL for the site http://extract.bbbike.org from which we can download the OSM data.

### 2. Sampling Design

* For each street (or road), starting from one end of the street, we split the street into .5 km segments till we reach the end of the street. (The last segment, or if the street is shorter than .5km, the only segment, can be shorter than .5 km.)

* Get the lat/long of starting and ending points of each of the segments. And assume that the street is a straight line between the .5 km segment.

* Next, create a database of all the segments

* Sample rows from the database and produce a CSV of the sampled segments

* Plot the lat/long --- filling all the area within the segment. These shaded regions are regions for which data needs to be collected.

### 3. Data Collection
Collect data on the highlighted segments.

## Installation

### Prerequisites

The package requires Python 3.10 or higher. Install the package from PyPI:

```bash
pip install geo-sampling
```

For development installation:

```bash
git clone https://github.com/geosensing/geo_sampling.git
cd geo_sampling
pip install -e .
```

## Usage

The package provides two main CLI commands:

### geo_roads
Process geographic regions to extract road segments:

```bash
geo_roads -c Singapore -n North -l 1
```

### sample_roads
Sample from processed road segments:

```bash
sample_roads input_roads.csv output_sample.csv --sample-size 100
```

## Documentation

* [Installation Guide](docs/install.rst)
* [Usage Examples](docs/usage.rst)  
* [Sample Workflow](docs/workflow.rst)

For complete documentation, visit the [project documentation page](https://geosensing.github.io/geo_sampling/).

## 🔗 Adjacent Repositories

- [geosensing/latlong-to-zip](https://github.com/geosensing/latlong-to-zip) — Reverse Geocode Lat/Long to Zip Codes using GeoNames, AskGeo, or Google. Compare Geocoding Services.
- [geosensing/geosensing.github.io](https://github.com/geosensing/geosensing.github.io) — Geosensing project website

## Authors

Suriyan Laohaprapanon and Gaurav Sood

## License

Scripts are released under the [MIT License](LICENSE).