Taskade: Multi-platform Planner And Task Manager

Have you ever heard of the binning technique?

My favorite cartographer is John M. Nelson. In fact, he's the one who actually got me searching what 'cartography' really is. Fortunately, he's a mix of a storyteller/technical support analyst/designer. So, his techniques are the ones I have least trouble understanding. And this is by no means a comment meant to offend because really, I'm a little slow and John is a very 'generous' teacher when it comes to explaining things; even through replies in posts. You can witness his work first hand at his own blog posts here;

https://adventuresinmapping.com/

So, the first of his work that captured my attention is the Six Month Drought of the American Southeast map created using the binning method. I didn't even know what binning is, but the map was so pretty it had me announcing my loyalty to #cartography hashtags. 

So what is binning? According to GIS Lounge, binning is a data modification technique where original data values is converted into a range of small intervals called bins. Bins will then be replaced with a values that is representative of that interval to reduce the number of data points. 

Okay. It should be a no-brainer. But the data he used was the polygon shapefiles of droughts' extent and their severity. Although it is still unknown to me how USGS actually collect this data but his map is sang the deserving anthem to their hard work. But alas, I never had the chance to reproduce it. I do not have the knack of identifying interesting data of any sort, so I either am stuck with reproducing a redundant work or waste my time in a wild goose chase for data; I'm a noob with a tunnel-vision focus. I won't even vote myself if we have a jungle excursion that requires mapping cause we'll be stuck longer than necessary. 

Even so, one year later, precisely this moment...I found a valid reason to attempt this. And it's all because I need to validate satellite imagery classification some colleagues made to show hot spots of global deforestation. I am not a remote sensing wizard, but vector data...now that I can work with. 

Using the same binning technique, I can summarize the steps as follows:

Merge all the data of deforestation variables  Generate hexagonal tessellation  Create the hexagon centroids  Use 'Spatial Join' to sum up the weights of overlapping polygon features of the merged data and join it with the hexagonal centroids   Then configure symbology 

Visualizing was a herculean effort for my brain. The map John made is a bivariate map. And compared to his data which has 2 numerical variables to enable that, mine only had one and it is the summation of the ranking weight I ensued on the deforestation variables. He merged all the shapefiles of weeks after weeks of drought severity readings. Me...I just manage this >>>

My first attempt was to just visualize the probability of the deforestation using the centroid point sizes.

That wasn't much of a success because visually, it doesn't actually appeal to my comprehension. It looks good when you zoom in closer because it gives off that newspaper print feel with that basemap. From this whole extent, it's not helpful.

So, after I tried to no avail to make it work with toggling the size and the colors, I found that instead of trying to make it look nice, I better opt on answering the questions posed by my colleague; could you identify the areas of high likeliness of prolonged deforestation? For that purpose, only hexagonal mesh would do the trick. So based on the 10 km sq size of their hexagons that depicts the areas of deforestation based on image classification, I used 'Spatial Join' too again and join the centroids back their predecessor hexagons to carry the binned values. 

Et voila!

The weight summation was of the degree of prolonged deforestation likeliness and the values range all the way to 24. I made 4 intervals which gave a practical visualization. Eight intervals were pushing it and 6 was not pleasant. It could be my color palette choice that made them unappealing but too many intervals will defeat my purpose. 

Yay or nay...I'm not too sure about it. But I do believe that this summarizes the areas where conservationists should be on the alert with. 

After having a discussion with a colleague, yeah...this technique has a lot of gaps. 

ONE; this is not a point feature. Using the values where the centroid touches/overlays ONLY is not exactly a precise method. Although, it is not wrong either.

TWO; The merged polygonal data came off as OVERLAPPING polygonal features. 

Overlooking the shortcomings and just using it to visually aid cross-checking...yea maybe. Even then...it's not as laser-point precise as one would aspire. I stand humbled. 

Story Map for Noobs: Cascade | WWF Network

Story Map is a web application template product that has been popularized in ArcGIS Online for a user-friendly and comprehensive narrative of maps. The ‘Cascade’ template has become the seamless interface of choice due to it’s ribbon transitions and availability of content streaming from external sources. 

Please refer to the following link for resources used in this webinar:

Story Map for Noobs: Cascade web application

📌 Availability: Retracted in 2021

🌱 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!

📚 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.

There is a moment where base maps just couldn't or wouldn't cut it. And DEMs are not helping. The beautiful hillshade raster generated from the hillshade tool can't help it if the DEM isn't as crisp as you would want it to be. And to think that I've been hiding into hermitage to learn how to 'soften' and cook visual 'occlusion' to make maps look seamlessly smooth. Cartographers are the MUAs of the satellite image community. 

I have always loved monochromatic maps where the visual is clean, the colors not harsh and easy for me to read. There was not much gig lately at work where map-making is concerned. The last one was back in April for some of our new strategy plans. So, when my pal wanted me to just 'edit' some maps she wanted to use, I can't stop myself with just changing the base map. 

The result isn't as much as I'd like it to be but then, we are catering the population that actually uses this map. Inspired by the beautiful map produced by John M Nelson that he graciously presented at 2019 NACIS; An Absurdly Tall Hiking Map of the Appalachian Trail. What I found is absurd is how little views this presentation have. The simplicity of the map is personally spot-on for me. Similar to Daniel P. Huffman as he confessed in his NACIS 2018 talk; Mapping in Monochrome, I am in favor of monochromatic color scheme. I absolutely loathe chaotic map that looked like my niece's unicorn just barf the 70s color deco all across the screen. Maybe for practical purposes of differentiating values of an attribute is deemed justifiable but surely...we can do better than clashing orange, purple and green together, no? 

So...a request to change some labels turn into a full-on make over. There are some things that I realized while making this map using ArcGIS Pro that I believe any ArcGIS Pro noob should know:

Sizing your symbols in Symbology should ideally be done in the Layout view. Trust me. It'll save you alot of time. 

When making outlines of anything at all, consider using a tone or two lighter than the darkest of colors and make the line thinner than 1 pt. 

Halo do matter for your labels or any textual elements of your map.

Sometimes, making borders for your map is justifiable goose chase. You don't particularly need it. Especially if the map is something you are going to compact together with articles or to be apart of a book etc. 

Using blue all the way might have been something I preferred but they have the different zonations for the rivers, so that plan went out the window. 

And speaking of window...the window for improvement in this map is as big as US and Europe combined. 

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

🌱 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.

zero to pandas

There are alot of Python courses out there that we can jump into and get started with. But to a certain extent in that attempt to learn the language, the process becomes unbearably long and frustratingly slow. We all know the feeling of wanting to run before we could learn how to walk; we really wanna get started with some subtantial project but we do not know enough to even call the data into the terminal for viewing.

Back in August, freeCodeCamp in collaboration with Jovian.ai, organized a very interesting 6-week MOOC called Data Analysis with Python: Zero to Pandas and as a self-proclaimed Python groupie, I pledged my allegiance!

If there are any expectation that I've managed to whizz myself through the course and obtained a certificate, nothing of that sort happened; I missed the deadline cause I was busy testing out every single code I found and work had my brain on overdrive. I can't...I just...can't. Even with the extension, I was short of 2 Pythonic answers required to earn the certificate. But don't mistake my blunders for the quality of the content this course has to offer; is worth every gratitude of its graduates!

Zero to Pandas MOOC is a course that spans over 6 weeks with one lecture webinar per week that compacts the basics of Python modules that are relevant in executing data analysis. Like the play on its name, this course assumes no prior knowledge in Python language and aims to teach prospective students the basics on Python language structure AND the steps in analyzing real data. The course does not pretend that data analytics is easy and cut-corners to simplify anything. It is a very 'honest' demonstration that effectively gives overly ambitious future data analysts a flick on the forehead about data analysis. Who are we kidding? Data analysis using programming language requires sturdy knowledge in some nifty codes clean, splice and feature engineer the raw data and real critical thinking on figuring out 'Pythonic' ways to answer analytical questions. What does it even mean by Pythonic ways? Please refer to this article by Robert Clark, How to be Pythonic and Why You Should Care. We can discuss it somewhere down the line, when I am more experienced to understand it better. But for now, Packt Hub has the more comprehensive simple answer; it simply is an adjective coined to describe a way/code/structure of a code that utilizes or take advantage of the Python idioms well and displays the natural fluency in the language.

The bottom line is, we want to be able to fully utilize Python in its context and using its idioms to analyze data.

The course is conducted at Jovian.ai platform by its founder; Aakash and it takes advantage of Jupyter-like notebook format; Binder, in addition to making the synchronization available at Kaggle and Google's Colab. Each webinar in this course spans over close to 2 hours and each week, there are assignments on the lecture given. The assignments are due in a week but given the very disproportionate ratio of students and instructors, there were some extensions on the submission dates that I truly was grateful for. Forum for students is available at Jovian to engage students into discussing their ideas and question and the teaching body also conducts office hours where students can actively ask questions.

The instructor's method of teaching is something I believe to be effective for technical learners. In each lectures, he will be teaching the codes and module requires to execute certain tasks in the thorough procedure of the data analysis task itself. From importing the .csv formatted data into Python to establishing navigation to the data repository...from explaining what the hell loops are to touching base with creating functions. All in the controlled context of two most important module for the real objective of this course; Numpy and Pandas.

My gain from this course is immensely vast and that's why I truly think that freeCodeCamp and Jovian.ai really put the word 'tea' to 'teachers'. Taking advantage of the fact that people are involuntarily quarantined in their house, this course is something that should not be placed aside in the 'LATER' basket. I managed to clear my head to understand what 'loop' is! So I do think it can solve the world's problem!

In conclusion, this is the best course I have ever completed (90%!) on data analysis using Python. I look forward to attending it again and really finish up that last coursework.

Oh. Did I not mention why I got stuck? It was the last coursework. We are required to demonstrate all the steps of data analysis on data of our choice, create 5 questions and answer them using what we've learned throughout the course. Easy eh? Well, I've always had the tendency of digging my own grave everytime I get awesome cool assignments. But I'm not saying I did not do it :). Have a look-see at this notebook and consider the possibilities you can grasp after you've completed the course. And that's just my work...I'm a standard C-grade student.

And the exciting latest news from Jovian.ai is that they have upcoming course at Jovian for Deep Learning called Deep Learning with PyTorch: Zero to GANS! That's actually yesterday's news since they organized it earlier this year...so yeah...this is an impending second cohort! Tentatively, the course will start on Nov 14th. Click the link below to sign-up and get ready to attack the nitty-gritty. Don't say I didn't warn ya.

And that's me, reporting live from the confinement of COVID pandemic somewhere in a developing country at Southeast Asia....

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