https://github.com/cityofsantamonica/lots-analysis

Analyzing parking lot data in Santa Monica
https://github.com/cityofsantamonica/lots-analysis

data-science open-data parking

Last synced: 22 days ago
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Analyzing parking lot data in Santa Monica

• Host: GitHub
• URL: https://github.com/cityofsantamonica/lots-analysis
• Owner: CityofSantaMonica
• Created: 2015-10-21T00:14:13.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2016-09-26T23:49:17.000Z (over 8 years ago)
• Last Synced: 2024-11-10T04:28:23.354Z (3 months ago)
• Topics: data-science, open-data, parking
• Language: Jupyter Notebook
• Homepage: https://data.smgov.net/Transportation/Parking-Lot-Counts/ng8m-khuz
• Size: 76.2 KB
• Stars: 2
• Watchers: 5
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# lots-analysis

A project to analyze parking lot data in Santa Monica.

The primary data source is the [City of Santa Monica's Open Data Portal](https://data.smgov.net),

and specifically the [Parking Lot Counts](https://data.smgov.net/Transportation/Parking-Lot-Counts/ng8m-khuz)

dataset.

This dataset is fed a snapshot of the [current state of the parking lots](https://parking.api.smgov.net/lots)

every 5 minutes, 24 hours a day.

## Setup

This project uses the [Data Science Toolbox](http://datasciencetoolbox.org/) to

quickly get an environment going.

### Prerequisites

First install the latest [Vagrant](https://www.vagrantup.com/) (at least `1.8.5`)

and [VirtualBox](https://www.virtualbox.org/) (at least `5.0.26`); these applications

support Linux, OSX, and Windows.

*Windows users*: use [Git BASH](https://git-for-windows.github.io/) for the commands below.

### Clone the repo

```bash

~$ git clone [email protected]:CityofSantaMonica/lots-analysis.git

...

~$ cd lots-analysis

```

### Setup and login to the vm

```bash

~/lots-analysis$ vagrant up

...

~/lots-analysis$ vagrant ssh

...

vagrant@data-science-toolbox:~$

```

### Setup environment and run the notebook server

```bash

vagrant@data-science-toolbox:~$ dst setup base

vagrant@data-science-toolbox:~$ sudo ipython notebook --profile=dst

```

Now you should be able to run the notebook in a browser (on the host machine):

[https://localhost:8888](https://localhost:8888) using the password you provided above.

You can safely ignore certificate warnings (the notebook server uses a self-signed cert.)

### Cleanup

When you are finished, release any resources used by the vm

```bash

vagrant@data-science-toolbox:~$ logout

...

~/lots-analysis$ vagrant destroy

```