Story Map For Noobs: Cascade | WWF Network

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! ♥

Please come back home

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! 🙆🏻‍♀️

Society of Conservation GIS Scholarship Award 2020

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....

survey123 offline

Survey123 for ArcGIS is perhaps, one of those applications that superficial nerds like me would like; it's easy to configure, kiddie-level degree of customization with 'coding' (for that fragile ego-stroke) and user-friendly template to use. 

No app development/coding experience is required to publish a survey form and believe it or not, you can, personalize your survey to not look so meh. 

It took me some time to stumble through the procedures of enabling this feature before I understand the 'ArcGIS Online' ecosystem to which this app is chained to. 

So how do we do it? And why doesn't it work pronto?

This issue may be due to the fact that when we first start creating our forms, we go through the generic step-by-step procedures that leave little to imagination what was happening. Most of the time, we're too eager to find out how it really work. 

When we publish a Survey123 form; be it from the Survey123 website portal or the Survey123 Connect for ArcGIS software, we are actually creating and publishing a folder that contains a hosted feature layer and a form. It is on that hosted feature layer that we add, delete, update or edit data it. From ArcGIS Online, it looks like any feature service that we publish out of ArcGIS Desktop or ArcGIS Pro, save for the special folder it is placed in with a 'Form' file. 

To enable any offline function in any hosted feature layer in ArcGIS Online, you will need to enable the 'Sync' feature. So far, in many technical articles that I have gone through to learn how to enable this offline feature always goes back to 'Prepare basemaps for offline use'. It is a tad bit frustrating. But my experience when deal with 'Collector for ArcGIS' gave me the sense of epiphany when it comes to Survey123. So when you have prepared your Survey123 form for offline usage and it still doesn't work...do not be alarmed and let's see how to rectify the issue. 

1. Locate your survey's hosted feature layer

At your ArcGIS Online home page, click 'Content' at the main tab. We're going to go directly to your hosted feature layer that was generated for your survey when you published. 

Locate your survey folder. Click it open 

In the survey folder, navigate to the survey's hosted feature layer and click 'Options' button; the triple ellipses icon

At at the dropdown, click 'View item details'. Please refer to the screenshot below: 

2. Change the hosted feature layer settings

At the item details page, navigate to the 'Settings' button at the main header and click it. This will prompt open the settings page for the feature layer. Refer to the screenshot below:

At the 'Settings' page, there are two tabs at the subheader; 'General' and 'Feature layer (hosted)'. Click 'Feature layer (hosted)' to configure its settings.

At the 'Feature layer (hosted)' option, locate the 'Editing' section. Here, check the 'Enable sync' option. This is the option that will enable offline data editing. Please refer to the following screenshot: 

Don't forget to click 'Save'

With this, your hosted feature layer which serves as the data model is enabled for synchronization. Synchronization helps to sync back any changes you've made when you're out on the field collecting data; editing, adding, deleting or update...depending on what feature editing you've configured. 

It's pretty easy once you get the hang of it and just bear in mind that the data hierarchy in the ArcGIS Online universe are as follows:

Feature layer (hosted) > Web map > Web application

Once you get that out of the way, go crazy with your data collection without any worries!

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

Taskade: Multi-platform planner and task manager

The year 2021 is looming over us and I am dying to have some sort of control over what I could be doing for the next 365 days. While 2020 had been a year of 'character building', I discover alot of things about everything around me and myself. For starters, I am an avid planner; surprisingly. But it does not mean that I follow through with them. See what I did right there? I am admitting the truth behind self-study and lifetime of learning.

With alot of things I have planned to breathe new life to my own progress and time management, I went hunting for some interesting stuff in the internet for inspiration and try-outs. And guess what? I found one and I think most people may have been using this already in full swing because the review is 5 ⭐!

🌑🌒🌓🌔🌕🌖🌗🌘🌑

Taskade is simply a project/team management tool. Ah ah ah...before you write me off, hear me out. Taskade is aimed to help teams to plan, organize or manage their tasks and prioritize output for decision-making. It is simply an interactive planner sans organizer sans dashboard that sees where you're at with your work, what you've managed to get done and communicate tasks among people in your team; IF you have a whole team working on some sort of project. Hence, the chat capability that is implemented in this tool.

At my job, I work in a team of only 2 people; me and another colleague, and we're the regional programme unit which is apart of the bigger unit of team mates spread elsewhere in other regions. So, just because your unit is small, it doesn't mean that your task load complements your pint-sized manpower. So, I've been looking for platforms that could help me organize our productivity and ensure high-quality output. Just because technology is more advanced, it doesn'e mean there isn't any learning curve, right? So I tried just about anything under the sun for project/team management; Asana, Slack, Discord, the pre-existing Google..., but none of them could nail all shortcomings precisely; due dates, assignment of tasks, progress, sub-tasks, interactive commenting, multiplatform sync, brainstorming etc. Channels in Slack gives me headache -- same with Discord, and Telegram channels is too 'static' and 'one-way street' for me to view everything.

I found Taskade after trying to find a complementary 'Forest: Focus' extension at the Google Chrome extensions marketplace. There are plenty of interesting high-quality extensions as of late and I am pleasantyl surprised because earlier this year, most of them were quite 'beta' in their functionality. I saw a 'Bullet Journal' extension that someone raved about and another individual commented: 'Isn't this Taskade?'. The curious cat I am, I googled it and was not disappointed. What are the main keywords that hooked me?:

FREE

Google-integrated

Remote work environment advocacy

Multi-platform

What features do Taskade actually have? ✨

Given that it is an All-in-One Collaboration tool, it is understandable if the GUI is pleasing on the eyes. I do understand that first-impression is everything; color, packaging, fore-front information and visual, but it was really the functionality that delivers me to salvation. If you're an active member of Dev.to, then you'll catch feels with this theme that Taskade delivers. Key features in Taskade that you should try out:

Task list

Collaborators invitation feature (no organizational handle required)

Chat feature (with a call feature!)

Workspace feature (nothing new but...I'll get back to this later)

5 interchangeable neural-forest task list templates; List, Board, Action, Mindmap and Org Chart -- seamless with no error.

The capability to utilize this very platform as a presentation or exported into PDF task list printout.

Safe to say, Taskade buried me alive with the curation of beautiful images for the background; again...not relevant but needed to be said.

The Live Demosandbo lets you try it out for yourself although, at first glance, you may be wondering what on earth you are looking at. But it won't take long before you discover that it is quite intuitive.

Did I mention you can download and access it from just about ANYWHERE? Laptops, browser extensions and even smartphone apps. I'm not kidding when I said Taskade is multiplatform; they work on Windows, Mac, Android, iOS and Linux. Currently, I am testing it out using the Chrome extension and installed the app in my Android phone. It works like I expect it to so far.

What is the difference between the FREE and PAID version? 💰💰💰

As I just mentioned, you can sign-up for it for free and use it for life...for free. The priced version is seemingly there to accommodate the file size per upload you require; as of now. For free plan, you can upload 5MB file per upload while the paid version increases the size to 50MB per upload. Both versions offer:

Unlimited storage

Unlimited tasks entry

Unlimited project creation

Unlimited collaborators addition

The development team is currently adding more functionalities such as Project Activity Tracking, Integration to Dropbox, Google Drive and One Drive as well as Email Integration -- available for free.

Although it is mentioned that the free version of Taskade includes unlimited tasks, collaborators and all essential features, it was also mentioned that you will need to upgrade if you exceed the workspace limits which doesn't actually have any entailing elaborations which I will try to dig soon enough. But safe to say that if you are a single person using this tool, you are considered a team of 'one' where your shared projects in workspace to your 'editors' are still considered free. Only workspace the addition of workspace members are billed. This may imply that there are certain limits to how many individuals you can add into your workspace before you are required to upgrade. So far, visually, I see that the limit may be 2 people that makes up to 3 people per workspace (including yourself). You can find some details to pricing and FAQs here:

Taskade | Simple Pricing

Personally, I don't think USD5 is a hard bargain if you're self-employed and work with external parties collaboratively. If you're apart of an organization, feel free to ask for demo from them. Discount is possible if you're from a nonprofit or educational institution.

How I use Taskade? ☕

Well, given that it was free to sign-up, I tried it out straight away and I'm happy to report that I successfully managed to use it without having to google nor view any how-tos. That is a good thing! In fact, I am quite elated with just how easy it is to use this tool that I have used my personal email to help centralize and manage my work and personal work side-by-side. If you prefer some satellite view of your progress and all the task you need to complete to clear off certain objective, this is not a bad organization.

So I created 2 workspace: one for work and one for my personal tasks. Then I just collate all my tasks into monthly projects.

My personal tasks involve me updating my study progress and curating stuff I like online into my Tumblr blog.

Create studyblr workspace

Create new project in the studyblr workspace to organize and brainstorm Tumblr contents I plan to create and post: Tumblr: 2021/01.

Utilize the Mindmap template from all the options of templates shared and start creating the and organizing the content I want and tasks I need to execute to develop them.

Et voila! There all there is to it! It is easy peasy and you can start adding due dates as reminders and links as resources as well as hashtags for filtering in future. Check out some drafting I did so far in the screenshots below!

For more updates, check out their Updates page that fully utilizes Taskade to share all the updates straight from December 2017 till present and the chat function is there available for you to ask the Taskade team about the feature updates directly. Now that's awesome cause you know something's good if the one who makes them, actually uses them.😎😎😎

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!