You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]



---
title: 'EarthPy: A Python package that makes it easier to explore and plot raster and vector data using open source Python tools.'
tags:
  - Python
  - gis
  - raster data
  - vector data
  - remote sensing
authors:
  - name: Leah Wasser
    orcid: 0000-0002-8177-6550
    affiliation: 1
  - name: Maxwell B. Joseph
    orcid: 0000-0002-7745-9990
    affiliation: 1
  - name: Joe McGlinchy
    orcid: 0000-0003-2135-0168
    affiliation: 1
  - name: Jenny Palomino
    orcid: 0000-0003-4879-9299
    affiliation: 1
  - name: Korinek, Nathan
    orcid: 0000-0003-0859-7246
    affiliation: 1
  - name: Chris Holdgraf
    orcid: 0000-0002-2391-0678
    affiliation: 2
  - name: Tim Head
    orcid: 0000-0003-0931-3698
    affiliation: 3
affiliations:
  - name: "Earth Lab, University of Colorado - Boulder"
    index: 1
  - name: "University of California - Berkeley, Project Jupyter"
    index: 2
  - name: "Wild Tree Tech"
    index: 3
date: 28 October 2019
bibliography: paper.bib
---

# Summary & Purpose

`EarthPy` makes commonly performed spatial data exploration tasks easier for scientist by building upon functions in the widely used packages: `Rasterio` and `GeoPandas`. `EarthPy` is designed for users who are new to `Python` and spatial data with a focus on scientific data.

When a user is working with spatial data for research, there are a suite of data exploration activities that are often performed including:

1. Viewing histograms and plots of single bands within a remote sensing image to explore potential calibration and other data quality issues.
2. Creating basemaps that have legends with unique symbology.
3. Creating plots of images with colorbars.
4. Rendering RGB (and other composite) images of multi band spectral remote sensing images.
5. Masking clouds from a remote sensing image.
6. Limiting geographic extent of spatial data

The above operations are crucial to understanding a dataset and identifying issues that may need to be addressed with further data processing when beginning an analysis. In the `R` world, these tasks are quickly performed using the `raster` and `sp` packages. However, there isn't a tool that makes these tasks easy for users in the `Python` open source package landscape.


## EarthPy Audience

`EarthPy` was originally designed to support the Earth Analytics Education program at Earth Lab - University of Colorado, Boulder. Our program teaches students how to work with a suite of earth and environmental data using open source `Python`. All lessons are published as free open education resources on our online learning portal (https://www.earthdatascience.org). Through this publication process, we identified that many spatial data exploration and cleanup tasks which were performed regularly required many steps that could be easily wrapped into helper functions. We modeled these functions after those available in the `R` ecosystem, given the experience of the developers' many years of working and teaching with `R`.

`EarthPy` allows the user to streamline common geospatial data operations in a modular way. This reduces the amount of repetitive coding required to open and stack raster datasets, crop the data to a defined area, and in particular, plotting data for investigation.   

# EarthPy Functionality

`EarthPy` is organized into several modules:

* [io: Input/output for data](https://earthpy.readthedocs.io/en/latest/api/earthpy.io.html): utility functions to download existing teaching data subsets or other data into a user's working directory (by default, this directory is: `~/earth-analytics/data`). The IO module supports downloading data for the Earth Lab Earth Analytics courses as well as any user with a URL to a compressed file.
* [mask: Mask out cloud and shadow covered pixels from raster data](https://earthpy.readthedocs.io/en/latest/api/earthpy.mask.html): helper functions to mask remote sensing images using a cloud mask or QA (i.e. quality) layer.
* [plot: Visualizing spatial data](https://earthpy.readthedocs.io/en/latest/api/earthpy.plot.html): plotting utilities including plotting a set of bands saved in a numpy array format, creating a custom colorbar, and custom legends with unique symbology.
* [spatial: Raster processing and analysis](https://earthpy.readthedocs.io/en/latest/api/earthpy.spatial.html): utilities to crop a set of bands to a defined spatial extent, create a hillshade, stack bands, and calculate normalized difference rasters.

## EarthPy Vignettes

In addition to detailed API documentation and example code executed by doctest,
`EarthPy` documentation includes a long-form [examples gallery](https://earthpy.readthedocs.io/en/latest/gallery_vignettes/index.html)
that demonstrates functionality using case studies. These longer case studies
provide an opportunity to document how to integrate the functionality contained in
different `EarthPy` modules, with an emphasis on compelling visualizations that
convey key concepts for spatial data processing.


# EarthPy in Context

## EarthPy Focus on Integration of Spatial Data By Scientists

`EarthPy` is an open source `Python` package that makes it easier to plot and work with both spatial raster and vector data using open source tools. `EarthPy`'s goal is to make working with spatial data easier for scientists who want to use open source `Python` tools for analysis and visualization.

`Earthpy` depends upon `GeoPandas` [@kelsey_jordahl_2019_3483425], which has a focus on vector data handling and analysis, and `Rasterio` [@gillies_2019], which facilitates input and output of raster data files as numpy arrays. It also requires `Matplotlib` for plotting operations.

To simplify dependency management and installation for non-experts, we maintain a version of EarthPy on the `conda-forge` channel, which installs the system
libraries upon which `EarthPy` depends. This combined with high-level wrapper
around `GeoPandas`, `Rasterio`, and `Matplotlib` [@Hunter:2007] lowers the barrier to entry for
people, particularly scientists, who are learning how to work with spatial data in
`Python`.

While there are other useful `Python` packages for working with vector data such as `PySAL` [@pysal2007] or raster data such as `GeoRasters`, `EarthPy` draws from both `GeoPandas` and `Rasterio` to integrate functionality for vector and raster into one package.


## EarthPy in the Classroom

`EarthPy` also supports education and teaching. The `io` module makes it easier for a student to download a suite of teaching data subsets and other data to a standard working directory (that is automatically created if it does not exist). This supports reproducibility of workflows in a classroom (or other) setting.

The `plot` module facilitates quick and early data exploration by introductory-level students for whom the intricancies of customizing plots with `Matplotlib` might be overwhelming. The `mask` and `spatial` modules both reduce the technical learning curve for spatial analysis with `Python`, which supports instructors in focusing on the key scientific concepts behind the code.

The vignettes developed with `EarthPy` also provide easily adaptable starting points for in-class exercises that help students learn key spatial concepts using scientific data.

# Acknowledgements

There have been many [contributors to earthpy](https://github.com/earthlab/earthpy/graphs/contributors) that we are thankful for. We are also thankful for the feedback that we recieved through the software review implemented by pyOpenSci. Specifically we thank Luiz Irber who has served as an editor for this review and the two reviewers: Sean Gillies and Rohit Goswami.

# References