Dancify Data Final Presentation

Untitled Document.md

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+# Dillinger
+## _The Last Markdown Editor, Ever_
+
+[![N|Solid](https://cldup.com/dTxpPi9lDf.thumb.png)](https://nodesource.com/products/nsolid)
+
+[![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger)
+
+Dillinger is a cloud-enabled, mobile-ready, offline-storage compatible,
+AngularJS-powered HTML5 Markdown editor.
+
+- Type some Markdown on the left
+- See HTML in the right
+- ✨Magic ✨
+
+## Features
+
+- Import a HTML file and watch it magically convert to Markdown
+- Drag and drop images (requires your Dropbox account be linked)
+- Import and save files from GitHub, Dropbox, Google Drive and One Drive
+- Drag and drop markdown and HTML files into Dillinger
+- Export documents as Markdown, HTML and PDF
+
+Markdown is a lightweight markup language based on the formatting conventions
+that people naturally use in email.
+As [John Gruber] writes on the [Markdown site][df1]
+
+> The overriding design goal for Markdown's
+> formatting syntax is to make it as readable
+> as possible. The idea is that a
+> Markdown-formatted document should be
+> publishable as-is, as plain text, without
+> looking like it's been marked up with tags
+> or formatting instructions.
+
+This text you see here is *actually- written in Markdown! To get a feel
+for Markdown's syntax, type some text into the left window and
+watch the results in the right.
+
+## Tech
+
+Dillinger uses a number of open source projects to work properly:
+
+- [AngularJS] - HTML enhanced for web apps!
+- [Ace Editor] - awesome web-based text editor
+- [markdown-it] - Markdown parser done right. Fast and easy to extend.
+- [Twitter Bootstrap] - great UI boilerplate for modern web apps
+- [node.js] - evented I/O for the backend
+- [Express] - fast node.js network app framework [@tjholowaychuk]
+- [Gulp] - the streaming build system
+- [Breakdance](https://breakdance.github.io/breakdance/) - HTML
+to Markdown converter
+- [jQuery] - duh
+
+And of course Dillinger itself is open source with a [public repository][dill]
+ on GitHub.
+
+## Installation
+
+Dillinger requires [Node.js](https://nodejs.org/) v10+ to run.
+
+Install the dependencies and devDependencies and start the server.
+
+```sh
+cd dillinger
+npm i
+node app
+```
+
+For production environments...
+
+```sh
+npm install --production
+NODE_ENV=production node app
+```
+
+## Plugins
+
+Dillinger is currently extended with the following plugins.
+Instructions on how to use them in your own application are linked below.
+
+| Plugin | README |
+| ------ | ------ |
+| Dropbox | [plugins/dropbox/README.md][PlDb] |
+| GitHub | [plugins/github/README.md][PlGh] |
+| Google Drive | [plugins/googledrive/README.md][PlGd] |
+| OneDrive | [plugins/onedrive/README.md][PlOd] |
+| Medium | [plugins/medium/README.md][PlMe] |
+| Google Analytics | [plugins/googleanalytics/README.md][PlGa] |
+
+## Development
+
+Want to contribute? Great!
+
+Dillinger uses Gulp + Webpack for fast developing.
+Make a change in your file and instantaneously see your updates!
+
+Open your favorite Terminal and run these commands.
+
+First Tab:
+
+```sh
+node app
+```
+
+Second Tab:
+
+```sh
+gulp watch
+```
+
+(optional) Third:
+
+```sh
+karma test
+```
+
+#### Building for source
+
+For production release:
+
+```sh
+gulp build --prod
+```
+
+Generating pre-built zip archives for distribution:
+
+```sh
+gulp build dist --prod
+```
+
+## Docker
+
+Dillinger is very easy to install and deploy in a Docker container.
+
+By default, the Docker will expose port 8080, so change this within the
+Dockerfile if necessary. When ready, simply use the Dockerfile to
+build the image.
+
+```sh
+cd dillinger
+docker build -t <youruser>/dillinger:${package.json.version} .
+```
+
+This will create the dillinger image and pull in the necessary dependencies.
+Be sure to swap out `${package.json.version}` with the actual
+version of Dillinger.
+
+Once done, run the Docker image and map the port to whatever you wish on
+your host. In this example, we simply map port 8000 of the host to
+port 8080 of the Docker (or whatever port was exposed in the Dockerfile):
+
+```sh
+docker run -d -p 8000:8080 --restart=always --cap-add=SYS_ADMIN --name=dillinger <youruser>/dillinger:${package.json.version}
+```
+
+> Note: `--capt-add=SYS-ADMIN` is required for PDF rendering.
+
+Verify the deployment by navigating to your server address in
+your preferred browser.
+
+```sh
+127.0.0.1:8000
+```
+
+## License
+
+MIT
+
+**Free Software, Hell Yeah!**
+
+[//]: # (These are reference links used in the body of this note and get stripped out when the markdown processor does its job. There is no need to format nicely because it shouldn't be seen. Thanks SO - http://stackoverflow.com/questions/4823468/store-comments-in-markdown-syntax)
+
+   [dill]: <https://github.com/joemccann/dillinger>
+   [git-repo-url]: <https://github.com/joemccann/dillinger.git>
+   [john gruber]: <http://daringfireball.net>
+   [df1]: <http://daringfireball.net/projects/markdown/>
+   [markdown-it]: <https://github.com/markdown-it/markdown-it>
+   [Ace Editor]: <http://ace.ajax.org>
+   [node.js]: <http://nodejs.org>
+   [Twitter Bootstrap]: <http://twitter.github.com/bootstrap/>
+   [jQuery]: <http://jquery.com>
+   [@tjholowaychuk]: <http://twitter.com/tjholowaychuk>
+   [express]: <http://expressjs.com>
+   [AngularJS]: <http://angularjs.org>
+   [Gulp]: <http://gulpjs.com>
+
+   [PlDb]: <https://github.com/joemccann/dillinger/tree/master/plugins/dropbox/README.md>
+   [PlGh]: <https://github.com/joemccann/dillinger/tree/master/plugins/github/README.md>
+   [PlGd]: <https://github.com/joemccann/dillinger/tree/master/plugins/googledrive/README.md>
+   [PlOd]: <https://github.com/joemccann/dillinger/tree/master/plugins/onedrive/README.md>
+   [PlMe]: <https://github.com/joemccann/dillinger/tree/master/plugins/medium/README.md>
+   [PlGa]: <https://github.com/RahulHP/dillinger/blob/master/plugins/googleanalytics/README.md>

idea1.md

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+Project Proposal: Local Temperature and Precipitation Trends Analysis in Cumberland County, PA 
+
+1. Research Question 
+
+How have average annual temperature, the frequency of extreme heat days (above 90°F), and total annual precipitation changed in Cumberland County, PA, over the past 50 years (1973–2023), and how are these trends expected to continue or accelerate in the next 30 years (2024–2054)? 
+
+Specifically, I aim to quantify the rate of change in average annual temperature, the frequency of extreme temperature events, and shifts in precipitation patterns. This project will provide actionable insights for public health officials, local farmers, and water resource managers to adapt to these changing climate conditions. 
+
+2. Data Source 
+
+The primary data source for this project will be the National Oceanic and Atmospheric Administration (NOAA) Climate Data Online portal, which provides access to historical weather data for the United States, including temperature and precipitation records. 
+
+Data Retrieval Process: The data will be accessed by querying the NOAA Climate Data Online portal, specifying Cumberland County, PA, as the location, with a time range from 1973 to 2023. Data will be downloaded in CSV format to then load into Python. 
+
+Variables in the Dataset: 
+
+Temperature Variables: Daily maximum temperature, daily minimum temperature, and daily average temperature. 
+
+Precipitation Variables: Daily total precipitation and the occurrence of extreme precipitation events (e.g., days with precipitation exceeding 1 inch). 
+
+Other Variables: Date, location, and potential weather station data 
+
+Variables to Be Created: 
+
+Annual/Seasonal Averages: Calculate annual and seasonal averages for temperature and precipitation. 
+
+Trends and Anomalies: Create variables that measure the rate of change over time and identify anomalies (unusually hot or wet years, etc.). 
+
+Extreme Event Frequency: Count the number of days per year with temperatures above 90°F and precipitation above 1 inch to identify the frequency of extreme weather events. 
+
+3. Data Analysis and Model Specification 
+
+Exploratory Data Analysis (EDA): 
+
+Initial analysis will involve data cleaning and visualization. Descriptive statistics and plots (line graphs, boxplots) will be used to understand the distribution of temperature and precipitation over time. Temporal trends can be assessed through moving averages and yearly comparisons. 
+
+Model Specification: 
+
+To project future trends, a time series regression model will be employed (thinking of using Ordinary Least Squares regression) with temperature and precipitation as dependent variables and time (year) as the independent variable. I can also explore the use of Autoregressive Integrated Moving Average (ARIMA) models for forecasting future climate patterns. 
+
+Implications for Stakeholders: 
+
+The results of this analysis will provide valuable insights for local stakeholders, such as agricultural managers, public health officials, and municipal planners, to anticipate and mitigate the impacts of climate change on farming and water management. For example, identifying an increasing trend in extreme heat days could prompt local authorities to invest in heat mitigation strategies or guide farmers on crop selection. Dickinson is also very involved with climate initiatives with Carlisle and Cumberland County, some which I have been a part of before. 
+
+Ethical, Legal, and Societal Implications: 
+
+Ethical considerations include ensuring that the data is used in ways that accurately represent the findings. There are no significant legal barriers to using NOAA data, as it is publicly accessible. However, the societal implications of the findings, such as the potential need for adaptation strategies, will be carefully communicated to ensure that stakeholders can make informed decisions. 
+
+