index.Rmd

---
title: Talk template
output:
  xaringan::moon_reader:
  #  self_contained: TRUE
    includes:
      in_header: conf/style-header.html
    lib_dir: assets
    seal: false
    css: ["default", "conf/css/style.css", "conf/css/fonts.css"]
    nature:
      highlightStyle: solarized-light
      highlightLines: true
      beforeInit: ["conf/js/gifffer.min.js", "conf/js/style-macros.js"]
---

```{r setup, echo = F}
knitr::opts_chunk$set(
  comment = "#",
  #cache = TRUE,
  collapse = TRUE,
  warning = FALSE,
  message = FALSE,
  fig.width = 7,
  fig.height = 5.25,
  fig.align = 'center',
  fig.retina = 3
)

# base plot layout
mypar = list(mar = c(3,3,0.5,0.5), mgp = c(1.5, 0.3, 0), tck = -.008)

# xaringan Extra
xaringanExtra::use_xaringan_extra(c("tile_view", "animate_css", "tachyons"))
xaringanExtra::use_extra_styles(
  hover_code_line = TRUE,         #<<
  mute_unhighlighted_code = TRUE  #<<
)
xaringanExtra::use_editable(expires = 1)
xaringanExtra::use_panelset()
```
class: middle, title-slide

<!-- top logo (comment to remove or edit on `conf/css/style.css:23`) -->
<div class="lab-logo"></div>
<!-- <div class="uni-logo"></div> -->

# Who is Willian Vieira?

<hr width="65%" align="left" size="0.3" color="#FFC800"></hr>

## and what has he been doing?

### Willian Vieira, .small[Analyst]

<br><br><br><br><br>
[<i class="fa fa-github fa-lg" style="color:#e7e8e2"></i> WV-Habitat/me](https://github.com/WV-Habitat/me)

[<i class="fa fa-twitter fa-lg" style="color:#e7e8e2"></i> @WillVieira90](https://twitter.com/willvieira90)

---

# Outline

- My thesis project

- The ECCC project

- Side projects

- Future projects

---
class: middle, center, inverse

# My PhD thesis

<hr width="100%" align="left" size="0.3" color="#FFC800"></hr>

---

.center[.font140[**How can we better predict tree species distribution?**]]

<br>

- Species Distribution Models (SDM) are not appropriated for trees

- But we can use *theory* to create **mechanistic models**

- What are the drivers of forest dynamics shaping tree distribution?

- Mathematical and statistical models to assess the effect of climate, competition, and forest management

---
# Chapter 1: State Transition Model

.center[![:scale 55%](https://raw.githubusercontent.com/willvieira/PhD/master/chapter1/img/fig1.png)]

- Derived from the metapopulation theory
- **B**oreal, **T**emperate, **M**ixed, and **R**egeneration ~ MAT + MAP
- Extension to include forest management practices
- Analytical equations, Matrix algebra, Jacobian approximation
- [STM R package](https://willvieira.github.io/STManaged/index.html); [Shiny App](https://github.com/willvieira/shiny_STM-managed)

.cite[Model from Vissault et al. [2020](https://onlinelibrary.wiley.com/doi/abs/10.1111/jbi.13978)]

---
# Chapter 2 & 3: Integral Projection Model

<br>
.center[![](images\ipm3.png)]

- Demographic models for 31 tree species from eastern North America
- $\lambda$ ~ [Growh + mortality + recruit] ~ climate + competition
- Non-linear hierarchical Bayesian models
- Stan: statistical programming language derived from C++
- High-performance computing (HPC)
- C.2: Random forest for sensitivity analysis
- C.3: Scale integration - from individual performance to the metapopulation

---
class: middle, center, inverse

# The ECCC project
<hr width="100%" align="left" size="0.3" color="#FFC800"></hr>

## Sampling design for monitoring boreal birds in Quebec

---
# BMS project

<br>
.pull-right[![](https://willvieira.github.io/sampling_BMS/studyArea_files/figure-html/fig-ecoregion-1.png)]


- National effort to develop a protocol to sample boreal birds
- Adapt and implement national design for the Quebec region
- Spatially stratified sampling approach weighted by:
  - Habitat
  - Cost
  - Legacy sites
- [Report](https://willvieira.github.io/sampling_BMS/)


---
# BMS project

.font130[R&D: A new approach to account for legacy sites]

.center[![:scale 60%](https://willvieira.github.io/sampling_BMS/legacy_files/figure-html/fig-sampleSize-example2-1.png)]

---
# BMS project

.font130[R&D: A new approach to account for legacy sites]

.center[![:scale 60%](https://willvieira.github.io/sampling_BMS/legacy_files/figure-html/fig-ecosExample-1.png)]

---
class: middle, center, inverse

# Side projects

<hr width="100%" align="left" size="0.3" color="#FFC800"></hr>

.font140[*From learning useless programming languages to automating unnecessary tasks*]

---
# Side projects

<br>

.font120[Reporting and templates]
- [Manuscript template](https://github.com/willvieira/ms_STM-managed) (using markdown, LaTeX, Pandoc, Lua)
- [Presentation template](https://github.com/willvieira/talkTemplate) (this presentation, some CSS and JavaScript)
- [Lab notebook](https://willvieira.github.io/book_forest-demography-IPM/)

.font120[Task automation]
- Make (reproducibility of manuscripts)
- Crontab ([Kijiji scraper R package](https://github.com/willvieira/KijijiScraper))
- GitHub Actions to automatically:
  - Test and build R packages
  - Reproduce analysis
  - Deploy reports and websites

---
class: middle, center, inverse

# Future projects

<hr width="100%" align="left" size="0.3" color="#FFC800"></hr>

.font140[*Or what I would like to explore*]

---
# Future projects

<br>

.font120[Modeling]
- Statistical models (spatiotemporal autocorrelation, time series)
- Machine learning beyond random forest

.font120[Development tools]
- Modularization (Packages, unity test, deployment, versioning, code review)
- Containerization (Docker)
- Data Engineering
- Cloud computing

---
class: middle, center, inverse

# Cloud-based geospatial technologies

<hr width="100%" align="left" size="0.3" color="#FFC800"></hr>

---
# Cloud-based geospatial technologies

<br>

.font120[**Cloud Optimized GeoTIFF (COG)**]

It is based on two complementary frameworks:
1. Optimal GeoTIFF storage (store and organize each pixel information)
  - Tiling
  - Overviews
2. HTTP GET range requests
  - extract only a portion of the GeoTIFF file
  - Not mandatory but already built into cloud services (Google, Azure)
  - Ranges are determined by external metadata

---
# Cloud-based geospatial technologies

<br>

.font120[**Spatial-Temporal Asset Catalog (STAC)**]
- A common language to describe geospatial information
- metadata standard (JSON)
- Structured metadata repository describing
  - **What** it is
  - **Where** data is located
  - **How** it can be used
-  Hierarchically structured into items, collections, and catalogs

Example with the Landsat images:
- RED band of an image is an asset
- All color bands of an image is an item
- All images together are a collection

---
# Cloud-based geospatial technologies

<br>

.font120[**Why should we move towards STAC?**]
- Once linked to STAC, we have access to any [new] open data effortless
- Catalog is centralized, providing easy searchability of new data sets
- Inclusion of new data is easy
- Community-based metadata extensions for specific problems

--

.font120[**Constrants**]
- It comes with high implementation costs (especially if we become data providers)
- Less useful if we are interested in only a few spatial datasets

---