azaleakamellia

Latest Posts by azaleakamellia

Peta Gunatanah Malaysia 2014 - 2018

Peta Gunatanah Malaysia 2014 -2018 ("Malaysia's Land Cover 2014 - 2018") web application is a platform generated for the Quality Assessment activity organized by Forest Research Institute Malaysia (FRIM) on 23rd June 2024.

The workshop aims to collect field/reference data from Malaysian's state agencies in the effort to verify the quality of the land cover classification output generated in support of CO2 release measurement from converted agricultural lands.

Participants are able access the app via conventional browsers from their mobile devices and submit drawings/sketches that they have captured within interactive data layers.

This web app aims to support direct input from source onto the task of improving the accuracy of the generated land cover maps. Vectors generated from this exercise are readily standardized with the required data scheme from quality assessment, making full use of the ArcGIS Online ecosystem full to a produce concrete output and actionable information.

🌱 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!

🧰 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!

🌱 Google Earth Engine 101

Uploading a shapefile as an asset in GEE and making use of it

🟢 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.

Google Earth Engine or lovingly called GEE is another free and open platform provided by Google to provide a very vast and comprehensive collection of earth observation data. Since Sentinel-2 is no longer available for download at USGS Earth Explorer, I find the alternative too challenging for me so GEE seems like the easiest way to go. If you're looking for a one-stop platform to access satellite imagery for free, GEE is a great place to start. You don't have to learn JavaScript explicitly to start using this tool.

🌱 Google Earth Pro 101

Google Earth Pro is one of the most powerful freely available software one can use for location investigation. If you're a non-tech GIS user who needs to know just enough to get your work going, then let's hit the ground running with this tutorial for starters.

🟢 Beginner-friendly.

🆓 Free with no hidden monetary cost.

🖥️ Available for Windows, Mac and Linux.

With this, I am commencing my submission for the #30DayMapChallenge for 2023 🗺

The categories outlined is similar to that of last year but I am never going to hate this repetition. How can I? It's a basics of making maps and there's so much to learn from the single-word theme.

Any aspiring map-makers out there? Let's share our maps for this wonderful month of November under the #30DayMapChallenge 2023!

Malaysia Bid Round 2023 (MBR 2023)

Tool: ArcGIS Pro 2.6.3 Technique: Symbolization, labeling and SQL expression

MBR 2023 is a peak event that culminates all the effort of data collection and stock take of hydrocarbon resource in the Malaysia. It is an annual event that put together all the exploration blocks, discoverable hydrocarbon fields and late life assets for upstream sectors to evaluate and invest in.

Leading up to the event, the Malaysia Petroleum Management (MPM) updates, re-evaluate and produces maps; static and digital, to cater to the need for the most update stock-take of information that can be gained from various source of exploration output; seismic, full tensor gradiometry, assets; cables, pipelines, platforms, as well as discoverable resources. This year's them aims to include various prospects and initiative to align the industry itself with lower carbon emission and to explore the option for carbon capture storage (CCS) attempts in the popular basins such as the Malay and Penyu Basin. This is a big follow-up with the closing of MBR 2022 with the PSC signing for 9 blocks a few days earlier.

Credit: Sh Shahira Wafa Syed Khairulmunir Wafa

Over ~70 maps for unique blocks have been produced during the finalization stage, ~210 maps during data evaluation and additional 20 for the event. And this excludes the standardized maps to formalize information requested by prospective bidders as well as clients who are facing prospects of extending their contract.

The standardization of the map requires the optimization of workflow and standard templates to cater to rapid changes and exporting to rapid output.

For more information on the event, please access the following resources:

PETRONAS: Malaysia Bid Round

PETRONAS myPROdata

The Malaysian Reserve: Petronas offers 10 exploration blocks in MBR 2023

Uninspired

I am a reckless uninspired person. I call myself a map-maker but I don't really get to make maps for reasons that I don't think I should venture outside of my requesters' requests. But mostly, I am compelled to get it right and I feel good if I can deliver what they need. The thing is, I no longer get spontaneously inspired to make maps anymore. Just as the rules become clearer the more you read books on cartography, fear just crop themselves up like 'Plant vs Zombies' 🌱 in PlayStation.

So, I am scared that I'm beginning to wear off my excitement about making map; really making them and not just knowing how to make them.

What sort of idea is great? I mean, what should I focus on trying to make? There are so many data out there that what I will attempt may be missing the train or just pale in comparison to other incredible work. I don't really mind it but I'm not that young to not understand self-esteem does ease the thinking process.

Can't say much, I mean...30 Days of Map Challenge hasn't been all that well with me. I should've prepared something before the event event started. I quit after the 3rd challenge cause I overthink and get panic attacks every time I feel I'm doing stuff half-ass.

Despite all that, I am lucky to have aggressively supportive siblings. They just can't seem to stop the tough love and always kicking me to just barf something out.

'It's the process that matters!'

When did I start forgetting how wonderful the process, huh?

The devil in the details

I have started to post some videos demonstrating some tools in ArcGIS Pro. Short ones and pretty quick ones which I strived for since I absolutely am frightened with the idea of irritating people with unnecessary voice-over. It has no garnered much response and it's cool with me. Although, the lack of traction does things to my insides, I go back to the real reason I am doing thing, which is to stash the tools that I managed to learn on my own by trials and errors and keep them somewhere I can refer back to it to remember how it works.

Creating maps involves a number of iterative processes made to suit the intended output. Although creating maps itself is a form of art; heavily reliant on target audience's knowledge and aesthetical preference, it is still an inherently democratic science. Thus, knowing the mainstream technology and tools in the industry to express your vision or message is given. So for those just starting out with using geographical information software (GIS) for your final year project or research, this videos are meant for you. The purpose is not to overwhelm you with too many information, or distract you with my narration, but to follow in real-time the process from the start up of the software to the running of tools that generates the information needed.

Knowing fully well that there is an endless variety of GIS software or tools out there, processes that you need to execute to make things happen may vary in name and functionalities. Forget the beef between ArcGIS and QGIS, of which one is the better tool; if it serves your needs, then use it. You're not obliged to pledge loyalty to software or brands although you are encouraged to maintain integrity in your beliefs when it comes to corporate versus open source tools in the industry. Both choices come with their advantages and disadvantages. Yours truly uses QGIS and ArcGIS Pro interchangeably. If it doesn't work in ArcGIS Pro, which I use primarily, I'll jump to using QGIS. It's not a big deal. If it works painlessly, there is no reason to feel bad about using it.

So far, the content I have made emphasizes mostly on ArcGIS Pro or Esri products since using them is how I come to learn more about geology and geography. QGIS was a name I did not learn of in my university years when ArcGIS versions start with the digit 9️⃣, so you can catch my drift.

We can go on and on about theoretical stuff and our smarter pals usually knows what to do when faced with the tools. Unfortunately, I fall in the percentile that needed to land on the job to understand what on earth I am supposed to do. This series of videos are for those who have the same problem as I do and need to see the magic actually happening before knowing what to do. And for the most part, there are so many things to read and try out before you get it right. So hopefully, the demos can kickstart some thoughts or observation in the logic within the software's ecosystem and become more than just a technical power-user.

This week, I touched on some tools that I found helpful when dealing with point vector data, so feel free to check it out 👇🏻

Next week, I'm thinking of exploring some series of point analysis and space time cube is beckoning for me to test it out. Until then, stay cool and drop a word if you need any clarifications on the demos!

[2022] 30 Day Map Challenge -- FAILED

Last year, I participated once again in the 30 Day Map Challenge that was going around in Twitter-ville come November. It is the 3rd attempt at the marathon and 2022 served as a reminder that progressed too despite getting stuck at Day 3 as life caught up with me.

I don't like the idea that I have left the challenge incomplete, again. It was not my priority and I work better with clear goals or visions of expected output. If it does not add to my need to learn something new ...it will be a task bound to head straight to the backburner. Let's resolve to make it a long-term routine instead of a spurt of stress trying to make the deadline.

As a consequence, I am attuning this task into one that actually gives me the benefit out putting into record the techniques and tools I used to make the maps in writing. I believe that will serve more purpose and added value other than visuals. And perhaps, have some stock ready for submission this year instead.

Anyone else participated in this challenge back in November? How did you do and what would you like to do better for the next one? Don't be shy and do drop a word or two.

In ArcGIS Pro, the Erase tool only comes with the Advanced license. There are other ways to go about removing parts of a polygon/line data layer like the Clip tool. But Union is that tool where it makes more sense by principle.

It works by marking overlapping parts of two different data layer with integers; 1, 2 and so forth. Those that do not overlap is universally -1. So, remove everything else that you want out of the picture by deleting output features that contain FID integer values of more than -1! Simple eh?

Check out the <3 minutes demo below!

P/S: Happy New Year peeps! ♥

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! 😁

📚 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.

🗻 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.

📑 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!

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 👇🏻

YouTube

🌏 Python: Geospatial Environment Setup 🎉

A deconstructed time-lapsed Python geospatial environment setup with `conda` from downloading necessary software to loading the Jupyter Note

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

Python: Geospatial Environment Setup (Part 1)

Here’s a quick run down of what you’re supposed to do to prepare yourself to use Python for data analysis.

Install Python ☑

Install Miniconda ☑

Install the basic Python libraries ☑

Create new environment for your workspace

Install geospatial Python libraries

🐍 Installing Python

Let’s cut to the chase. It’s December 14th, 2021. Python 3 is currently at 3.10.1 version. It’s a great milestone for Python 3 but there were heresay of issues concerning 3.10 when it comes to using it with conda. Since we’re using conda for our Python libraries and environment management, we stay safe by installing Python 3.9.5.

Download 👉🏻 Python 3.10.1 if you want to give a hand at some adventurous troubleshooting

Or download 👉🏻 Python 3.9.5 for something quite fuss-free

📌 During installation, don’t forget to ✔ the option Add Python 3.x to PATH. This enables you to access your Python from the command prompt.

Installing Miniconda

As a beginner, you’ll be informed that Anaconda is the easiest Python library manager GUI to implement conda and where it contains all the core and scientific libraries you ever need for your data analysis upon installation. So far, I believe it’s unnecessarily heavy, the GUI isn’t too friendly and I don’t use most of the pre-installed libraries. So after a few years in the darkness about it, I resorted to jump-ship and use the skimped version of conda; Miniconda.

Yes, it does come with the warning that you should have some sort of experience with Python to know what core libraries you need. And that’s the beauty of it. We’ll get to installing those libraries in the next section.

◾ If you’re skeptical about installing libraries from scratch, you can download 👉🏻 Anaconda Individual Edition directly and install it without issues; it takes some time to download due to the big file and a tad bit longer to install.

◾ Download 👉🏻 Miniconda if you’re up to the challenge.

📌 After you’ve installed Miniconda, you will find that it is installed under the Anaconda folder at your Windows Start. By this time, you will already have Python 3 and Anaconda ready in your computer. Next we’ll jump into installing the basic Python libraries necessary for core data analysis and create an environment to house the geospatial libraries.

📚 Installing core Python libraries

Core libraries for data analysis in Python are the followings:

🔺 numpy: a Python library that enables scientific computing by handling multidimensional array objects, or masked objects including matrices and all the mathematical processes involved.

🔺 pandas: enables the handling of ‘relational’ or 'labeled’ data structure in a flexible and intuitive manner. Basically enables the handling of data in a tabular structure similar to what we see in Excel.

🔺matplotlib: a robust library that helps with the visualization of data; static, animated or interactive. It’s a fun library to explore.

🔺 seaborn: another visualization library that is built based on matplotlib which is more high-level and produces more crowd-appealing visualization. Subject to preference though.

🔺 jupyter lab: a web-based user interface for Project Jupyter where you can work with documents, text editors, terminals and or Jupyter Notebooks. We are installing this library to tap into the notebook package that is available with this library installation

To start installing:

1️⃣ At Start, access the Anaconda folder > Select Anaconda Prompt (miniconda3)

2️⃣ An Anaconda Prompt window similar to Windows command prompt will open > Navigate to the folder you would like to keep your analytics workspace using the following common command prompt codes:

◽ To backtrack folder location 👇🏻

◽ Change the current drive, to x drive 👇🏻

◽ Navigate to certain folders of interest e.g deeper from Lea folder i.e Lea\folder_x\folder_y 👇🏻

3️⃣ Once navigated to the folder of choice, you can start installing all of the libraries in a single command as follows:

The command above will enable the simultaneous installation of all the essential Python libraries needed by any data scientists.

💀 Should there be any issues during the installation such as uncharacteristically long installation time; 1 hour is stretching it, press Ctrl + c to cancel any pending processes and proceed to retry by installing the library one by one i.e

Once you manage to go through the installation of the basic Python libraries above, you are half way there! With these packages, you are already set to actually make some pretty serious data analysis. The numpy, pandas and matplotlib libraries are the triple threat for exploratory data analysis (EDA) processes and the jupyter lab library provides the documentation sans coding notebook that is shareable and editable among team mates or colleagues.

Since we’re the folks who like to make ourselves miserable with the spatial details of our data, we will climb up another 2 hurdles to creating a geospatial workspace using conda and installing the libraries needed for geospatial EDA.

If you're issues following the steps here, check out the real-time demonstration of the installations at this link 👇🏻

See you guys in part 2 soon!

Community Empowerment Strategy Dashboard 2021 | WWF-Malaysia

Tool: Operations Dashboard ArcGIS, Survey123 for ArcGIS, ArcGIS Online Technique: XLSForm programming, web application development

The northern highland communities of Lun Bawang have been collaborating with WWF-Malaysia under the Sarawak Conservation Programme (SCP) to empower sustainable economies and managing their natural biodiversity through the Community Empowerment Strategy (formerly known as Community Engagement and Education Strategy).

Since 2016, the communities have been actively mapping out their land uses and culturally important locations to delineate their areas of settlement and source of livelihood. Given the close vicinity of their communities to the licensed timber concessions, producing a definitive map is important to preserve and conserve their surrounding natural capitals.

Several outreach has been done and the community mapping effort has been shifted to implement citizen science via the Survey123 for ArcGIS mobile application which is apart of the ArcGIS ecosystem. This enables the local community to collect information despite the lack of network reception and the data can still be synchronized upon availability automatically or manually shared with the field officers.

📌 Availability: Retracted in 2021

Malaysia Forest Cover 2020 | WWF-Malaysia

Tool: ArcGIS Pro 2.9.3, Operations Dashboard ArcGIS & ArcGIS Online Technique: Data transformation and geometric calculation

WWF-Malaysia Forest Cover Baseline is a dashboard of forest cover extent status in selected land uses across Malaysia's region, methodology of analysis and resources involved in the exercise.

The WWF-Malaysia Forest Cover Baseline and Forest Cover Key Performance Index (KPI) is a task undertaken by the Conservation Geographical Information System (CGIS) Unit to amass the discrete information of forest cover extent across Malaysia's 3 main region of legislation: Peninsular Malaysia, Sarawak and Sabah. This exercise produces a concise dashboard report in an online platform that describes the processed information on the forest cover status as well as their prospective areas identified for conservation work.

Report can be interactively accessed at the following:

The dashboard can be accessed at Malaysia Forest Cover 2020.

📌 Availability: Retracted in 2021

Eyes on the Forest Sarawak | WWF-Malaysia

The ‘Eyes on the Forest’ Sarawak web application is developed with the joint effort of WWF-Malaysia and WWF-Japan. Emulating the successful ‘Eyes on the Forest’ Sumatra database, this web app intends to provide an undiluted information on the rich diversity of Sarawak’s unique wildlife, forests and land uses as well as increasing the transparency on the threats; deforestation, infrastructure developments and urbanization, as well as its drivers; concessions, agricultural activities and unsupervised resource harvesting.

Identifying where the threats, the drivers and the conservation targets are located will help in making a concrete point at the policy level and push for more boots on the ground to ensure that the protected areas stay protected. The baseline information provided are essential in the prompt intervention for biodiversity protection.

Data collected and consolidated from dispersed public sources and engineered for simple and direct visualization. Values are generated based on verified legislated information provided by stakeholders and state agencies to the Sarawak Conservation Programme (SCP) for WWF-Malaysia.

Developed and maintained by:

Azalea Kamellia

GIS Officer (SCP) | WWF- Malaysia

June 2018 - Present

how to read research paper effectively 📚

I'm hitting the backed-up reading list that I've accumulated in my Zotero. It's annoying and you procrastinate the task of reading as much as possible when you're in that potato phase. I am demotivated, bored, constantly tired, and feel like devoting myself to reading storybooks for life. If I can get paid for all the hours I sleep every time I feel like signing out from life, I could be making a decent living. But, too bad, I don't.

I do not endorse any products or review anything since I feel like, to each, your own. So, I'm not going to tell you what works best or how some tips can magically fix your life. I am lucky that I have an incredible academic supervisor, a flexible boss at work, a very academic-oriented sibling, and a supportive squad of friends. Even with all that, I am still depressed. So, if you're down on the low at the moment, you're not alone. But when you have made a promise, you will look like a total flake if you don't deliver. So, you gotta move your ass anyway, right?

I just started reading papers again and it was so hard. Two weeks go by without me making any progress...just stuck on one paper and not retaining a single piece of information at all. All that forehead and nothing...nothing sticks. So you can say that I am hating life right now. But, today...I manage to reach some sort of compromise with myself and it starts to feel good. So, I would like to share it with you guys who could be struggling to get the engine started as well.

🎯 Literature Review Catalog

My supervisor is an awesome human being. He's the manager/cheerleader/mentor/Allfather/Captain America/Britney Spears to my lackluster academic history. He had been keeping tabs on me despite my intermittent anxious mood that swings like a freaking metronome, so you can say that he practically keeps my boat afloat at this unprecedented time. For our proposal writing (there's a whole army of us that he's supervising), he shared something valuable. The 'Literature Review Catalog'.

Yes. It's an Excel Sheet. Nothing fancy with very normal columns that indicates the papers/resource you've read. Looks simple and useful. The columns are populated as follows:

Year: The year of publication.

Author: Short author list.

Country (Study Area): The areas that are being studied in this research. If you're an Earth Science student like me, you can narrow it down to countries. But I think overall, countries are the most general part of discriminating different studies.

Main Keyword: I create my own keywords to develop my own system of comprehension. But I do create a column for the keywords found in the paper itself.

Issue & Objectives: You can find this information from the Abstract and Introduction part of the paper.

Proposed Method: This can be found in the Results section but I usually scan through the Methodology to add in more information when I do second round scanning of the paper.

Findings & Conclusions: I add in more notes on information that is new to me here in addition to the conclusion. New information can be extracted when you do another once-over of the paper and a conclusion can be obtained from the Conclusion section.

Reference: You can find references that are relevant to your studies from this paper! So why not? Right?

But, it's the laborious work that comes with it that turns my stomach. It scares the hell out of me despite any motivational speech I give myself. But it can all make sense when you pair it with the following method 👇🏻👇🏻👇🏻

🎯 How To Read A Paper Quickly & Effectively | Easy Research Reading Technique

This is the gem my sister told me about yesterday. I brushed it off since it stresses me out to see people sharing their speed-reading techniques, study tips, and how to ace all the subjects in the world or how to get a 4.0 GPA. It really isn't the good people's fault and I blame it on my constantly anxious self. I don't even know what's wrong with me, so...it's not them. It's me. But, here, we're gonna work on 'me'. So, give this 10 minutes video a watch. It's worth it because Dr. Amina Yonis really knows what she's talking about and what's even better, she really is an advocate for effective reading/studying. It's short enough for you to maintain your attention span and you will learn how to actually 'evaluate' your reading materials; are they worth the second shot at reading? Is there any added value to it?

To summarize, what you should look out for:

Title: Read the title and find the keywords

Abstract: Lookout for the results and methods in a simple sentence

Introduction: Read the first and last paragraphs. Most of the time, the first paragraph highlights the satellite view of the crisis and the last paragraph zooms straight for the objective.

Results: Pay attention to the headings since that more or less highlights what was it that they find. If there aren't any headings, try looking at them by paragraph. Scan them through.

Conclusion: This summarizes everything in the research paper.

After the 'Conclusion', you may feel like it is an info/findings that you've already expected or grasped, and you may just proceed and read other new ones in your pile. But if you need to dive deeper, jump to the 'Results' again for the key figures or results and limitations.

So ...

How do you go about reading this and what has it got to do with the 'Literature Review Catalog'? Well, using this efficient reading method and taking out the notes into the columns will help you condense all the important information and helps you stop re-reading constantly the details that are not paramount to your study.

🎯 Forest App

To amp up and see if it was effective, I actually timed myself with the 'Forest App'. I have been estranged from it since my potato phase, but now, it's back to being that BFF I need. It took 10 minutes to go through all the steps and if the paper isn't heavy-laden, 5 minutes to fill it into the 'Literature Review Catalog'. I manage to think and ask questions in my head as I fill in the columns and I believe that's the most important part of the effective reading that we need as someone who's jumping into a very dynamic environment of scrutinizing existing work. You can use any sort of timer to actually give a sense of urgency to your work - it does help to a certain extent. So, if you intend to have fun creating a forest of pretty trees while making good of your focus time, check out this video!

🎯 Reference Manager

And please please please, organize/record your references responsibly using reference management software. Some swears by Mendeley, or the good ol' EndNote. There's also Flowcite and Citationsy. Use them. Don't download those papers indiscriminately without recording the details that can help you sync them straight to your word processor using viable plugins. I personally use Zotero. It comes with a Chrome plugin and Microsoft Word plugin that you can download separately. It's compatible with Linux and iOS operating system. I used to park my work at Mendeley, but I find Zotero more powerful and flexible enough to use and it actually helps me to make the effort to remember what I actually downloaded rather than rely on the convenience of going back and forth to cloud storage. And it's open-source. So, try it out to create an organized library.

To all the aspiring scholars out there, when you win, we all win. Share your phase and troubles with the #studyblr or here with me. Emotional support is important and if the internet does not give you peace of mind, sign out and unplug. It's ok. When you're ready to work, reach out to anyone you think will respond positively and want to help you succeed. We can't all do things alone. So, start that power-up playlist and start working!

33rd National Geoscience Conference 2021 (NGC 2021)

Tool: ArcGIS Pro, ArcGIS Pro Deep Learning extension, Python, Jupyter Notebook Technique: Deep learning; semantic segmentation, cartography, remote sensing

The presentation of abstract outlining the implementation of deep learning in land cover classification across the Borneo island. It uses the Sentinel-2 image data and the band combination that differentiates the bareland, tree cover as well as waterbodies and croplands whilst training the U-Net model using the referenced data collected.

Please find the abstract published here:

Warta Geologi, Vol. 47, No. 1, April 2021

The presentation slide can be accessed at the following link 👇🏻:

train on water

take care

Studying is hard enough when you are fighting the onset of hormonal hurricane of emotions while you're a teenager. Then you have you formative 5 to 6 years of collegiate years where you learn that your idealistic notions of the world is just plain snobbish and that you're one of the many that finds satisfaction in pushing your ideas into other people's head. Doing all that while studying, it was hard too.

Then comes the job hunting, climbing ladders to nowhere from one workplace to another...just drifting. That was the vacation off from studying and just wanting to find stability.

Then one day, you broke your heart at the same time you get an opportunity to a decent work somewhere. You ran. Then it was about understanding this whole new dimension of knowledge that you've fallen in love with. It was like your first love came back to find you and tell you he's back and that he won't leave you alone anymore. So, back to the books you fall into.

So yeah...studying is a rollercoaster of emotions. Learning is a lifelong journey. You're never too old to learn something new. And then phrase where you can't teach old dogs new tricks is just plain inaccurate. You can teach them new tricks and they can do it if they want to. It's the matter of either they want to do it or if their body can still keep up with them.

If you're an older student, I feel you. You feel left behind at times and frustrated because you feel like you can't catch up with things. If you're someone who's starting to work on research studies, it may be a harder state of mind. Because you're almost always on your own lamenting on you're comprehension of theories and questioning if you get whatever you absorbed right. So...it is hard. How do you know that you're alright and that you're not spinning down the road of doomed isolation? You don't. These are some of the glaring things I think are the universal problems among people who, with the courage of a lion, answered the call of hermitage and subject themselves to an occupation so feared that it is deemed over once high school ends; student:

Wardrobe reduction to daily uniform of hoodies/sweaters and jog-pants/sweatpants

It's totally ok. If you're wearing the same one everyday for the rest of the week, you still win. It's practical, it's easy and for hijabsters, the hoodie helps with instant full head cover while you run to receive those stuff you endlessly ordered online. There's nothing to be ashamed off.

💡I say: Shower 🚿. Every. Day. Twice a day is most ideal. Stay fresh and hygienic. If you're not going anywhere nor are you working out in those attire, then you're good. At least for a week. When you shower, you're giving your body a break from the exhausting task of slouching, the heat of your brain going overdrive and your own face breaking out from the stress. So yeah...shower. Wash your face. Brush your teeth. You're gonna be at risk of letting yourself go when you're left alone too comfortable in your own company. If you're an introvert, it feels like heaven but it doesn't mean you should be at the risk of losing out on the joys of prepping yourself to look good once in a while. Regularly make the effort to find decent clothes you'll use to go out on a Saturday window shopping and kick back to relax. On the rare occasion you get a break from your endless paper chasing, Zoom calls and writing, you can use your wardrobe clearing day to match few clothing and have a runway at home yourself or for a date with yourself to the Netflix movie you're gonna watch on your laptop. Who says you can't dress up to do online shopping? That's the only way you'll know that you do not need that new shade of Forencos lipstick you saw the other day. Duh.

Sudden weight gain/loss

I can't say that is unexpected especially if you're the kind who can go long without exercises and the type who munch while you work (which is a bad habit by the way). But sometimes you can't help if especially if you live on your own or have your own living space. Being on your own makes it kind of easy to get off-track about the norm of things; how much have you eaten today...is that your 5th coffee...should you be finishing that leftovers...etc. It is harder if you don't have your own kitchen and rely on packed food or processed ones.

💡I say: I can assure you, it is NOT unusual. This happens often. Each one of us have different eating habits. There isn't a flexible advise to cater this problem. But it helps to think that when you're setting up a study environment, you're setting up a living environment that enable you to be inspired enough for productivity. I make it a point to have food available but at a good distance and exercise attire/equipment nearby. Better yet, wear them. To curb that crazy cravings, I try my best to have my meal on-time; breakfast at 8 am, lunch at 12 pm or 1 pm etc. Eat what you love, but in moderation. Hydrate regularly. It's hard, I know. All the chips🍟 and chocolate chip cookies🍪 and sweetened 3-in-1 lattes☕...I just...well, just remember...in moderation and you're a human. Don't beat yourself up if you eat them anyway.

Back pain, knee pain, frozen shoulder...everything hurts

I have a hard time tearing away from what I am doing for the 20-minutes-work-5-minutes-rest rule but I try my best to maintain good posture. The bad habit of crouching and slouching at desk jobs is a universal problem. At the onset of the pandemic, for the first time in my life, my back felt strained. I panicked and started to seriously stretch myself and found that not only I am killing my back, I was not breathing properly when I am doing my work due to my posture.

💡I say: Don't break the bank trying to buy new furniture or tools because from experience, you end up not actually using them for the purpose you bought them in the first place. Eg. treadmill...you're more likely to hang your clothes or towels there...or super expensive yoga mats. Carpet works just fine too.

Standing desk is deemed to have little to no effects on spinal/muscle health by some studies but I went on ahead and made a make-shift standing desk. I found that I'm more alert when I work standing and breathe better. There was no contest...standing desk is a choice made. My standing table is an old dining table that I turned into my bed where I put my portable laptop desk. So, it also works as a barre that I used to stretch my legs for that mid-splits I've been working on.

These are some of the stuff that plague me daily and how I cope. How about you guys? How do you take care of yourself as someone who willingly serve yourself on a platter to tertiary educational torture to console your ever-thirsty curious mind?

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! 🙆🏻‍♀️

mapshaper

Ok. 

I wanna know why have I never heard of this online tool before. Like, what the hell is wrong with the social media? Is something wrong with Twitter or Instagram or something that they never caught on mapshaper? Or was it just me and my hazardous ignorance, yet again?

Have you tried this free nifty online tool that literally simplify crazy complicated shapefile polygons like it’s no one’s business?!

It started with some last minute inspiration on how to collate data from 3 different regions; developed from remote sensing techniques which vary from one another. The common output here is to turn all of them into a vector file; namely shapefile, and start working on the attribute to ease merging of the different shapefile layers.

Once merged, this shapefile is to be published as a hosted feature layer into the ArcGIS Online platform and incorporated into a webmap that serves as a reference data to configure/design a dashboard. What is a dashboard? It's basically an app template in ArcGIS Online that summarizes all the important information in your spatial data. It's a fun app to create, no coding skills required. Check out the gallery here for reference:

Operations Dashboard for ArcGIS Gallery

There are two common ways to publish hosted feature layer into ArcGIS Online platform.

Method 1: Zip up the shapefile and upload it as your content. This will trigger the command inquiring if you would like to publish it as a hosted feature layer. You click 'Yes' and give it a name and et voila! You have successfully publish a hosted feature layer.

Method 2: From an ArcGIS Desktop or ArcGIS Pro, you publish them as feature service (as ArcMap calls them) or web layer (as the its sister ArcGIS Pro calls them). Fill up the details and enabling the function then hit 'Publish' and it will be in the platform should there be no error or conflicting issues.

So, what was the deal with me and mapshaper? 

🛑 A fair warning here and please read these bullet points very carefully:

I need you to remember...I absolve any responsibility of what happens to your data should you misinterpreted the steps I shared. 

Please always  👏🏻  BACK 👏🏻 UP  👏🏻  YOUR 👏🏻 DATA. Don’t even try attempting any tools or procedure that I am sharing without doing so. Please. Cause I am an analyst too and hearing someone else forget to save their data or create a backup is enough to make me die a little inside. 

For this tool, please export out the attribute table of your shapefile because this tool will CHANGE YOUR SHAPEFILE ATTRIBUTES. 

When I was publishing the vector I have cleaned and feature-engineered via ArcGIS Pro...it took so long that I was literally dying inside. I'm not talking about 20 minutes or an hour. It took more than 12 hours and it did not conjure the 'Successfully published' notification as I would've expected from it.

So at around 5.30 am, I randomly type 'simplify shapefily online free'. Lo and behold, there was mapshaper.

All I did was, zip up my polygon, drag it to the homepage and it will bring you to the option of choosing the actions that will be executed while the data is being imported into mapshaper:

detect line intersections

snap vertices

This option will help you to detect the intersections of lines within your vector/shapefile. This can help identify topological error.

The option to snap vertices will snap together points of similar or almost identical coordinate system. But it does not work with TopoJSON formats.

There is something interesting about this options too; you can enter other types of customized options provided by the tool from its command line interface! But hold your horses peeps. I did not explore that because here, we want to fix an issue and we'll focus on that first. I checked both options and import them in.

This will bring the to a page where there you can start configuring options and method to simplify your vector.

To simplify your shapefile, you can have both options to prevent the shape of the polygon being compromised; prevent shape removal, and to utilize the planar Cartesian geometry instead of the usual geoid longitude and latitude; use planar geometry. The implication of the second option is not obvious to me yet since all I wanted was to get the data simplified for easy upload and clean topology, thus, I chose both options to maintain the shape and visibility of all my features despite the highest degree of simplification.

Alike to the options of methodology for simplication in the mainstream software, I can see familiar names:

Douglas-Peuker

Visvalingam / effective area

Visvalingam / weighted area

First and foremost, I had no slightest idea of what these were. Like for real. I used to just go first for the default to understand what sort of output it will bring me. But here, the default; Visvalingam / weighted area, seemed like the best option. What are these methodologies of simplification? There are just algorithms used to help simplify your vectors:

🎯 Douglas-Peucker algorithm decimates a curve composed of line segments to a similar curve with fewer points (Ramer-Douglas-Peucker algorithm, Wikipedia; 2021).

🎯 Visvalingam algorithm is a line simplication operator that works eliminating any less significant points of the line based on effective area concept. That basically means that the triangle formed by each of the line points with two of its immediate neighboring points (Visvalingam Algorithm | aplitop).

🎯 Visvalingam algorithm with weight area is another version of Visvalingam algorithm of subsequent development where an alternative metrics is used and weighted to take into account the shape (Visvalingam & Whelan, 2016).

For reasons I can't even explain, I configured my methodology to utilize the third option and now that I have the time to google it, Thank God I did.

Then, see and play with the magic at the 'Settings' slider where you can adjust and view the simplification made onto the vector! I adjusted it to 5%. The shape retained beautifully. And please bear in mind, this vector was converted from a raster. So, what I really wanted is the simplified version of the cleaned data and to have them uploaded.

Now that you've simplified it, export it into a zipped folder of shapefile and you can use it like any other shapefile after you extracted it.

Remember when I say you have got to export your table of attributes out before you use this tool? Yea...that's the thing. The attribute table will shock you cause it'll be empty. Literally. With only the OBJECTID left. Now, with that attribute table you've backed up, use the 'Join Table' tool in ArcGIS Pro or ArcMap and join the attribute table in without any issues.

Phewh!!

I know that it has alot more functions than this but hey, I'm just getting started. Have you ever done anything more rocket science than I did like 2 days ago, please share it with the rest of us. Cause I gotta say, this thing is cray!! Love it so much.

mapshaper developer, if you're seeing this, I 🤟🏻 you!

UPDATE

I have been asked about the confidentiality of the data. I think this is where you understand the reason behind the fact that they will work even with using just the ‘.shp’ file of the shapefile since _that_ is the vector portion of the shapefile. 

Shapefile is a spatial data format that is actually made up of 4 files; minimum. Each of these files share the same name with different extensions; .prj, .shx, .shp and .dbf. Although I am not familiar with what .shx actually accounts for, the rest of them are pretty straightforward:

.prj: stores the projection information

.dbf: stores the tabulated attributes of each features in the vector file

.shp: stores the shape/vector information of the shapefile. 

So, as the tool indicate, it actually helps with the vector aspect of your data which is crucial in cartography. 

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!

Don't break the chain peeps! Reblog cause I'm looking for inspiration for my next masterpiece! 🙇🙇🙇

reblog/like if you’re an active studyblr/langblr

I’ve just unfollowed a bunch of inactive blogs, now that I follow ONLY 54 blogs??? pls reblog/like so I can have an active dashboard and new friends hehehe