How To Read Research Paper Effectively 📚

🗻 Diorama Drama

So, John Nelson made this absolutely simple tutorial on how to create a diorama interesting features on Earth's surfaces. I have been eyeing alot of people making them and resisting the urge to make one. I mean, I have the habit of falling into a rabbit hole with making maps that I'll definitely be having too much fun to stop. That could mean hours and hours scrutinizing colors, perspective or setups and even legend arrangements. But this...I decided not to overdo it and just make one.

I can't believe how easy it is to make one! Here's a piece of many to come; the elevation diorama of area within the Batang Ai Dam and Batang Ai National Park in Sarawak, East Malaysia.

What you'll learn... 1️⃣ How to extract raster from existing image layer in Living Atlas; that's how we extracted the DEM layer for this elevation data.

2️⃣ How to create a 3D scene in ArcGIS Pro and navigate through them.

3️⃣ How to create a layout layer and export them as a static image format i.e jpeg, png, tif etc...

Check out the video tutorial here 👇🏻

I was thinking that this tutorial is making me feel like there is a possibility of creating some floating island or pixel-like models of isolated pieces of land -- my head is imagining some kind of dragons flying around in some nonexistent Viking fantasy map. But hey...if you're not inspired to create crazier maps from John's tutorial, then are you even a cartographer at all?

He's on Twitter, LinkedIn, YouTube and he even has this supercool blog of his; Adventures in Mapping, where he shares all of his tutorials in full documentation and gorgeous portfolio that could only consist of magic! One such as I can only aspire.

So, that was what our Sunday looked like and I think I'll be playing around with dioramas for a few weeks. Let's see just how many of these I can make 'til the next Sunday.

📚 Nature in the Heart of Borneo (2020)

Tool: ArcGIS Pro 2.6.1

Technique: Annotation, Labeling and Symbology

A series of maps were created for the book published by WWF-Malaysia and FORMADAT (Forum Masyarakat Adat Dataran Tinggi Borneo) back in 2020 called Nature in the Heart of Borneo.

This book was meant as a guide to some of the natural attractions at Northern parts of Sarawak. If it was clear, Northern Sarawak is where the we have our very own highlanders which consist of primarily the Lundayeh/Lun Bawang, Sa'ban and Kelabit people. Some of the beautiful settlements up in the north that should not be missed are Ba'kelalan and Long Semadoh. They have beautiful homestays and even more beautiful landscapes with trekking activities lined up for tourists. And this is the culmination of ardent passion by my two absolutely wonderful colleagues, Alicia Ng and Cynthia Chin.

Most part of the maps were made using readily available basemap provided by Esri in their Living Atlas. But in entirety, many of the features and details are drawn manually within ArcGIS Pro. Like many other mapmakers out there, the labeling feature is horrendously temperamental and I either end up using annotations instead.

In summary, technically, there are 2 lessons learned here:

1️⃣ Establish concept or pick an idea before you start drawing

A concept of the map and palette should be established at the earliest stage possible. And don't just throw the task of making maps and split them evenly between cartographers. They won't have similar ideas or similar interpretations of the concept. It'll only give you double the pain of creating the maps again from scratch.

2️⃣ Omit borders

If you're making maps for books, don't border trying to make borders and fully utilize the whole layout. In the end, you'll need to export out your maps and they will resize it anyway and it'll compromise the maps you created. As if it wasn't graining enough in the first place, it'll look absolutely microscopic by the time they're done.

Yes peeps. I’ve been studying and on contrary to all my previous attempts to make beautiful notes, I say f it and just work with what helps me clear my head the fastest 🏃🏻‍♀️. I love writing notes, but I realize, to gather my thoughts properly, I need some sort of way to not waste paper just to arrange and rearrange my ideas or comprehension of things. 

What better way of doing that than using a mind map!

So you kiddos out there who are starting out with Python and just can’t wait to get into deep learning or machine learning, I’d say, hold your horses for a minute and have some preview of that pond you’re trying to jump into. And don’t be scared, cause we’re all friends here in the hell-hole of learning plateau. Will it get better? I believe so. I am positive I understand more of the principles of deep learning and the relevance of Python libraries associated with it. Yes...this is a Python bar, darling. 👩🏻‍💻

There’s no real shortcut if you ask me since we have different way of comprehending things; my pre-existing mold may have harder time grasping the things I am learning right now than you would. So don’t be afraid to doodle while you think. No amount of paper will be enough to help you understand things, so better start being sustainable by using some digital platforms and saving those papers to when you’re truly ready to pen out your understanding of things; not what you read. There’s a difference!

Check out the mind map of some essential Python libraries you can get started with before you start doing some deep learning. It’s worth reviewing all that prior, I promise. 

Have fun! 🙆🏻‍♀️

🧰 Publicly available data

Hunting for spatial data comes naturally now. There seems to be less and less opportunity for doubts when we could attach a pair of coordinates to some places.

For work and hobby, hunting for data take almost half of the usable hours I set aside to execute certain objectives; if not 100%. Although the internet is a vast plain of data, not all of them are usable. The democratization of data is a subject that is to translucent to discuss but to solid to argue with. Thus, with differing opinions, we get different versions of them online. Here are some of the interesting data platforms I manage to scour based on their thematic subject

🌳 Nature and Environment

Delta at Risk - Profiling Risk and Sustainability of Coastal Deltas of the World. I found this while lamenting on how people love asking for data addition into their maps at the eleventh hour. I find their confidence in my skills quite misleading but flattering nonetheless. But it does not make it any less troublesome.

Protected Planet - Discover the world's protected and conserved areas. This platform includes not just data of protected areas, but also other effective area-based conservation measures like ICCAs IUCN listing and as the website claims, it is updated regular via submissions from agencies. So far, I found this platform to be the most convenient since it rounds up all possible conservation-based themes which also includes World Heritage Sites.

Global Forest Change (2000-2020) - The global forest extent change since 2000 to the current year or lovingly referred to as the Hansen data by most forestry RS specialist. This data is updated annually and to be honest, the platforms are literally everywhere. But this platform is legitimate under Earth Engine Apps and you can refer to Google Earth Engine for future data updates to ease your search.

👩‍⚖️ Administrative Data

GADM - Map and spatial data for all countries and their sub-divisions.

🏦 Built-environment Data

OpenStreet Map - This database is the most amazing feat of tech-aware crowdsourcing. A little more than 2 decades ago, some 'experienced' gate-keeping professionals would have refuted its legitimacy within an inch of their lives but OSM has proven that time prevails when it comes to bringing the accessibility and network data into practical use. I am not that adept with downloading from this website so I go directly to a more manual data download. My favorite is the Geofabrik Download but you can also try Planet OSM.

🎮 Other Cool Data

OpenCell ID - Open database platform of global cell towers. Cleaning the data is a nightmare but I think it is just me. I have little patience for cerebral stuff.

So, those are some of the data I managed to dig for personal projects. Hope it helps you guys too!

📑 International Climate Initiative (IKI) Land Use Plan: Green Initiative in the Heart of Borneo (HoB) Report

Tool: ArcGIS Pro 2.9.3 Technique: Overlay analysis, visualization via remote sensing technique

These maps are developed to aid or supplement the Natural Capital Valuation (NatCap) initiative. As cited by WWF:

An essential element of the Natural Capital Project is developing tools that help decision makers protect biodiversity and ecosystem services.

One of the site included in this initiative by WWF-Malaysia is the Heart of Borneo (HoB). Specifically for this exercise, the visualization of policy and land use eventually become the data input utilized in the tool InVest that generates the models and maps for the economic values of ecosystem services within the landscape of interest.

The generation of the data mainly includes superficial remote sensing to assess the status of the land use in the respective concessions using Sentinel-2 satellite image with specific band combination to identify tree cover, particularly mangrove forest.

Python: Geospatial Environment Setup (Part 2)

Python: Geospatial Environment Setup (Part 2)

Hey again folks! I am here for the second part of Python environmental setup for a geospatial workspace. I published the first part of this post two weeks ago. So if you've not yet read that, I'll catch you up to speed with our checklist:

Install Python ☑

Install Miniconda ☑

Install the basic Python libraries ☑

Create a new environment for your workspace

Install geospatial Python libraries

🗃 Create a new environment for your workspace

Since we have actually manually set up our base environment quite thoroughly with all the basic libraries needed, to make our work easier, we can just clone the base environment and install all the additional essential libraries needed for geospatial analysis. This new environment will be called geopy. Feel free to use a name you identify most with.

Why don't we just create a new environment? Well, it means we have to start installing the Python libraries again from scratch. Although it is no trouble to do so, we want to avoid installing so many libraries all at once. As I mentioned in Part 1, there is always a risk where incomplete dependencies in one library will affect the installation of other libraries that you intend to install in one go. Since we already have a stable and usable base environment, we can proceed to use it as a sort of pre-made skeleton that we will build our geospatial workspace with.

1️⃣ At the Anaconda Command Prompt, type the following:

2️⃣ Press Enter and the environment will be clone for you. Once it is done, you can use the following command to check the availability of your environment 👇🏻

You should be able to see your geopy environment listed along with the base environment.

👩🏻‍💻 Install geospatial Python libraries

Here we will proceed with the installation of a few geospatial Python libraries that are essential to reading and exploring the vectors and rasters.

🔺 fiona: This library is the core that some of the more updated libraries depend on. It is a simple and straightforward library that reads and writes spatial data in the common Python IOs without relying on the infamous GDAL's OGR classes.

🔺 shapely: shapely library features the capability to manipulate and edit spatial vector data in the planar geometric plane. It is one of the core libraries that recent geospatial Python libraries rely on to enable the reading and editing of vector data.

🔺 pyproj: is the Python interface for the cartographic projections and coordinate system libraries. Another main library that enables the 'location' characteristics in your spatial data to be read.

🔺 rasterio: reads and writes raster formats and provides a Python API based on Numpy N-dimensional arrays and GeoJSON.

🔺 geopandas: extends the pandas library to allow spatial operations on the geometric spatial data i.e shapefiles.

💀 As you might have noticed, we won't be doing any direct gdal library installation. It's mainly due to the fact that its installation is a process that seems to be accompanied by misery at every turn and involved workarounds that are pretty inconsistent for different individuals. Does it mean that we won't be using it for our Pythonic geospatial analysis? Heck no. But we will be taking advantage of the automatic dependency installation that comes with all the libraries above. The rasterio library depends on gdal and by installing it, we integrate the gdal library indirectly into our geospatial environment. I found that this method is the most fool-proof. Let's proceed to the installation of these libraries.

1️⃣ At the Anaconda Command Prompt, should you start from the beginning, ensure that your geopy environment is activated. If not, proceed to use the following command to activate geopy.

Once activated, we can install the libraries mentioned one after another. Nevertheless, you also have the option of installing them in one go directly using a single command 👇🏻

💀 geopandas is not included in this line-up NOT because we do not need it. It's another temperamental library that I prefer to isolate and install individually. If gdal is a rabid dog...then geopandas is a feral cat. You never know how-when-why it doesn't like you and forces a single 10-minute installation drag to hours.

3️⃣ Once you're done with installing the first line-up above, proceed with our feral cat below 👇🏻

4️⃣ Use the conda list command again to check if all the libraries have been installed successfully.

🎉Et voilá! Tahniah! You did it!🎉

🎯 The Jupyter Notebook

It should be the end of the road for the helluva task of creating the geospatial environment. But you're going to ask how to start using it anyway. To access this libraries and start analyzing, we can easily use the simple and straight-forward Jupyter Notebook. There are so many IDE choices out there but for data analysis, Jupyter Notebook suffices for me so far and if you are not familiar with Markdown, this tool will ease you into it slowly.

Jupyter Notebook can be installed in your geopy environment as follows:

And proceed to use it by prompting it open via the command prompt

It ain't that bad, right? If you're still having problems with the steps, do check out the real-time video I created to demonstrate the installation. And feel free to share with us what sort of problems you have encountered and the workaround or solutions you implemented! It's almost never a straight line with this, trust me. As mentioned in the previous post, check out the quick demo below 👇🏻

See you guys again for another session on geospatial Python soon!

The Forest App: Build a forest with your focus

Forest: Stay Focus

🔍 Background

In my line of work, there are alot of things that require Google search that could stretch for hours as I slid down one rabbit hole after another. So, I used timer to actually remind me if I am spending too much time on certain things that I am researching. True enough, there are many options available; real kitchen timer, phone, watch, smart-watch, computer alarm etc. But since it’s a new year and there are lots of new apps popping up in the recommendation from Playstore, I noticed how the trend of tracking your habits is becoming more popular despite the pandemic. You would expect that the pandemic will slow people down career-wise or personal upkeep, but the technological development just won’t leave alone. They’ve served you with contactless transaction and centralized delivery system…now, they’re telling you that you could keep yourself in check by tracking your habits.

I used to utilize Hourglass app that I installed in my computer to time my work. It is free and you can customize it; the time duration, name of the time allocated and change the color of the interface. Simple and easy to use. Quite generic. But I wonder just how much time I spent with doing my research or study or rest? Hourglass doesn’t record the time spent on the tasks so there’s no recall or record feature. And there isn’t much to customize in Hourglass since it is pretty much what-you-see-is-what-you-get.

❓ What sort of features we would like to have in a timer at this age?

There has got to be more to life, no? I mean, we can do better with 64-bit resolution and all. Stretching out the functionality may be unnecessary but I don’t mind exploring.

Having a visually pleasant interface for the timer would be a plus but what I needed was a record of all the time spent on doing of the focused work I did for certain tasks. And I with that sort of feature, it would be great if it can be synchronized in multiple platform so that I can extend the usage to not only when I am face with my own laptop.

So I randomly scrolled though the Playstore and found that the app Forest: Stay Focus by Seekrtech. It is a productivity tool that helps you allocate limited time for certain subject or work in intervals with rest time or so. Most people like to refer this sort of technique as Pomodoro, which I believe is not something new; 25 minutes of focused work/ study followed by 5 minutes rest before repeating it again for as much cycle as you want. Nevertheless, long before Pomodoro, we’ve been taught that 30 minutes is the limit of non-stop focus if you intent to maximize information retention.

❓ What does Forest app offer?

Forest app, for all intent and purposes, is a timer and a fancy one at that. In fact, it gamifies the 'productivity’ challenge to a new level. By using the tree-growing concept.

_“You focus in your allocated time, your tree will reach full growth. Do it many times, you’ll have a forest”

And it’s not hard to stay motivated with this app. With its pleasant vibrant but muted colors, it's easy on eyes and kind to your mind.

“Forest app is pretty. Cheerful muted colors with a concept that encourages you to use it more.”

You are visually growing a tree with your dedication or focus to your work. The more time you spent focusing, the more coins and badges you accumulate. There are many types of trees that you can 'purchase’ with the coins you gain as you successfully focus during the allocated time you have set. And the trees have interesting designs with limited time designs offered all year round.

What happens when you quit before you finish the time allocated? Well, your tree more or less die. You’ll find a tree stump to prove it.

🕹️ Gamify productivity, at global scale

There’s a feature in Forest app called Plant Mode where you can enable the Deep Focus Mode where you will be guided back to the app if you attempt to open any app in your phone that isn’t in the allowed list. But here’s the catch; you’ll only be able to create an 'Allowed List’ if you’re a Pro user. But even without it, it’s not necessary if your goal is to stay focused, no?

Are you feeling lonely planting trees on your own? You can create a 'focus group’ room by enabling the Plant Together mode. In this mode your friends, families or team will be focusing on the tasks/ subject of interest when you create the room session. Of course, they too need to have the app installed to be able to participate. The one who quits first will 'reward’ everyone will the same tree stump. Yes, you win and lose together. Pretty cool way to motivate the gang for any study prep time eh?

“Add your friends with existing Forest app account via their registered email address and start your focus group.”

Are you a lone ranger looking for a 'nemesis’ to compete with? No worries, there’s the global leaderboard called Deep Focus Mode Ranking. You can see the ranking of all global users’ focused time for the week for your motivation. Compete with the rest of the global population in Deep Focus Mode weekly and relish in seeing yourself in the ranks.

In summary, the Forest app is more than a cute app to grow trees. Total win in the concept and reward system.

Aspect Details 🎨 Visual Nurturning and simple ⭕ 🎮 Concept Nurturing and simple ⭕ 📊 Record Forest: Daily, monthly & yearly ⭕ 🏅 Reward Very motivating ⭕ 🔄 Synchronization Chrome & Firefox extension and app ⭕

Real Forest

Forest app has become the sponsor for Trees for the Future organization that plant trees globally in impoverished countries with degraded lands with Forest Garden Programs in Cameroon, Kenya, Senegal, Uganda and Tanzania. By using this app, you will be able to accumulate coins that you can donate to the organization to plant a tree in the participating regions. The threshold is 2500 coins.

Not too bad considering that you are trying to cure your phone addiction, practice self-restrain and counter climate change with reforestation.

💰 Price

It is a one-time lifetime purchase at only RM10.50 at Google Playstore ~$USD 2. That’s it. No subscription fees.

🎯 Conclusion

All of the features that I have shared are enabled with Pro version which I can assure you, isn’t a waste to invest.

You can be part of the NGO program that helps global reforestation.

Have detailed record and statistics of your productivity

Configure allow list for apps that you need for your work

Synchronize your data everywhere and have a safe backup

Access to more collection of visually pleasing trees

Participate in online events and obtain more visual rewards for your personal collection

Compete with friends

Have customized tags for your tasks; you can include ALOT of things for this, trust me

Compete in more challenges

No ads. Period.

Have you ever used Forest before? If you did, do share with me how you optimize it for your productivity!

Split by Attributes GP tool....when would you actually use this?

There are times when you're making a map but symbolizing using the symbology feature is not enough to characterize the data visually. Thus, having this tool makes cartographical work a little easier by generating copies of the original data, split into separate layers based on the attribute that we need. By doing this, it makes the task of adding the legend much easier in the layout as well.

Most often, when making maps for slide presentation, you would want to segregate data into separate layers with certain uniform values for a certain attribute and a create a new data layer which we can use over and over again.

Although definition query can help with visualizing and showing the features with the attribute value that we want, we may want to create a separate data to avoid compromising the original data or constantly repeating the task of typing/configuring the SQL commands.

This tool is valid for shapefiles and feature classes. Any other data types may need to be converted into those two formats before you can run it. Check out the long-winded demo below:

Since this tool is actually a Python script, it can be integrated into a code for batch geoprocessing or model for iteration over many data layers or interconnection to other tools; automation at its full-on glory! 😁

Please come back home

🌱 Google Earth Engine 101

Viewing, filtering and downloading Landsat 9 satellite image

🟢 Beginner-friendly.

🆓 Free with no hidden monetary cost.

🤚🏻 Requires registration so sign-up 👉🏻https://signup.earthengine.google.com/, access via browser and Internet connection

🖥️ Available for Windows, Mac and Linux.

If you're down the rabbit hole with the how-to documents on #googleearthengine and still want to find a simple basic code to start with, feel free to check out this 16-minute video I've made for myself to remember some of the syntaxes. There's no better way to understand it than doing it yourself so, I've also included links to the script app and code in GitHub 👇🏻

⌨️ Code: https://github.com/hrllscrt/gee/tree/...

🌏 GEE sandbox: https://code.earthengine.google.com/a...

The script demonstrates how to utilize readily available data in the Earth Engine data repository like LSIB 2017 for demarcation and I've taken the liberty to play around with Landsat 9. The code should be applicable for Landsat 8 too, for those who wants to use it to extract data straight away.

Don't be shy to share your questions if you have any and have fun dissecting the data!