In ArcGIS Pro, The Erase Tool Only Comes With The Advanced License. There Are Other Ways To Go About

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

🌱 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.

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

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

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?

[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.

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. 

📚 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.

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!