#Python for QGIS

[myjondaleh]

Mastering QGIS Desktop 3.34 LTR: The Ultimate GIS Guide

English Video tutorial, just for $9.99 (NORMAL price $19.9). Get it once and you will get endless access right. Master QGIS Desktop 3.34 LTR with this step-by-step guide designed for GIS professionals and enthusiasts. Learn essential workflows, advanced mapping techniques, geoprocessing tools, and automation for powerful spatial data management. Elevate your GIS expertise today!

[esoxy]

So let's get into the nitty-gritty technical details behind my latest project, the National Blue Trail round-trip search application available here:

This project has been fun with me learning a lot about plenty of technologies, including QGis, PostGIS, pgRouting, GTFS files, OpenLayers, OpenTripPlanner and Vita.

So let's start!

In most of my previous GIS projects I have always used custom made tools written in ruby or Javascript and never really tried any of the "proper" GIS tools, so it was a good opportunity for me to learn a bit of QGIS. I hoped I could do most of the work there, but soon realized it's not fully up to the job, so I had to extend the bits to other tools at the end. For most purposes I used QGis to import data from various sources, and export the results to PostGIS, then do the calculations in PostGIS, re-import the results from there and save them into GeoJSON. For this workflow QGIS was pretty okay to use. I also managed to use it for some minor editing as well.

I did really hope I could avoid PostGIS, and do all of the calculation inside QGIS, but its routing engine is both slow, and simply not designed for multiple uses. For example after importing the map of Hungary and trying to find a single route between two points it took around 10-15 minutes just to build the routing map, then a couple seconds to calculate the actual route. There is no way to save the routing map (at least I didn't find any that did not involve coding in Python), so if you want to calculate the routes again you had to wait the 10-15 minute of tree building once more. Since I had to calculate around 20.000 of routes at least, I quickly realized this will simply never work out.

I did find the QNEAT3 plugin which did allow one to do a N-M search of routes between two set of points, but it was both too slow and very disk space intense. It also calculated many more routes than needed, as you couldn't add a filter. In the end it took 23 hours for it to calculate the routes AND it created a temporary file of more than 300Gb in the process. After realizing I made a mistake in the input files I quickly realized I won't wait this time again and started looking at PostGIS + pgRouting instead.

Before we move over to them two very important lessons I learned in QGIS:

There is no auto-save. If you forget to save and then 2 hours later QGIS crashes for no reason then you have to restart your work

Any layer that is in editing mode is not getting saved when you press the save button. So even if you don't forget to save by pressing CTRL/CMD+S every 5 seconds like every sane person who used Adobe products ever in their lifetimes does, you will still lose your work two hours later when QGIS finally crashes if you did not exit the editing mode for all of the layers

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So let's move on to PostGIS.

It's been a while since I last used PostGIS - it was around 11 years ago for a web based object tracking project - but it was fairly easy to get it going. Importing data from QGIS (more specifically pushing data from QGIS to PostGIS) was pretty convenient, so I could fill up the tables with the relevant points and lines quite easily. The only hard part was getting pgRouting working, mostly because there aren't any good tutorials on how to import OpenStreetMap data into it. I did find a blog post that used a freeware (not open source) tool to do this, and another project that seems dead (last update was 2 years ago) but at least it was open source, and actually worked well. You can find the scripts I used on the GitHub page's README.

Using pgRouting was okay - documentation is a bit hard to read as it's more of a specification, but I did find the relevant examples useful. It also supports both A* search (which is much quicker than plain Dijsktra on a 2D map) and searching between N*M points with a filter applied, so I hoped it will be quicker than QGIS, but I never expected how quick it was - it only took 5 seconds to calculate the same results it took QGIS 23 hours and 300GB of disk space! Next time I have a GIS project I'm fairly certain I will not shy away from using PostGIS for calculations.

There were a couple of hard parts though, most notably:

ST_Collect will nicely merge multiple lines into one single large line, but the direction of that line looked a bit random, so I had to add some extra code to fix it later.

ST_Split was similarly quite okay to use (although it took me a while to realize I needed to use ST_Snap with proper settings for it to work), but yet again the ordering of the segments were off a slight bit, but I was too lazy to fix it with code - I just updated the wrong values by hand.

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The next project I had never used in the past was OpenTripPlanner. I did have a public transport project a couple years ago but back then tools like this and the required public databases were very hard to come by, so I opted into using Google's APIs (with a hard limit to make sure this will never be more expensive than the free tier Google gives you each month), but I have again been blown away how good tooling has become since then. GTFS files are readily available for a lot of sources (although not all - MAV, the Hungarian Railways has it for example behind a registration paywall, and although English bus companies are required to publish this by law - and do it nicely, Scottish ones don't always do it, and even if they do finding them is not always easy. Looks to be something I should push within my party of choice as my foray into politics)

There are a couple of caveats with OpenTripPlanner, the main one being it does require a lot of RAM. Getting the Hungarian map, and the timetables from both Volánbusz (the state operated coach company) and BKK (the public transport company of Budapest) required around 13GB of RAM - and by default docker was only given 8, so it did crash at first with me not realizing why.

The interface of OpenTripPlanner is also a bit too simple, and it was fairly hard for me to stop it from giving me trips that only involve walking - I deliberately wanted it to only search between bus stops involving actual bus travel as the walking part I had already done using PostGIS. I did however check if I could have used OpenTripPlanner for that part as well, and while it did work somewhat it didn't really give optimal results for my use case, so I was relieved the time I spend in QGIS - PostGIS was not in vain.

The API of OpenTripPlanner was pretty neat though, it did mimic Google's route searching API as much as possible which I used in the past so parsing the results was quite easy.

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Once we had all of the data ready, the final bit was converting it to something I can use in JavaScript. For this I used my trusted scripting language I use for such occasion for almost 20 years now: ruby. The only interesting part here was the use of Encoded Polylines (which is Google's standard of sending LineString information over inside JSON files), but yet again I did find enough tools to handle this pretty obscure format.

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Final part was the display. While I usually used Leaflet in the past I really wanted to try OpenLayers, I had another project I had not yet finished where Leaflet was simply too slow for the data, and I had a very quick look at OpenLayers and saw it could display it with an acceptable performance, so I believed it might be a good opportunity for me to learn it. It was pretty okay, although I do believe transparent layers seem to be pretty slow under it without WebGL rendering, and I could not get WebGL working as it is still only available as a preview with no documentation (and the interface has changed completely in the last 2 months since I last looked at it). In any case OpenLayers was still a good choice - it had built in support for Encoded Polylines, GPX Export, Feature selection by hovering, and a nice styling API. It also required me to use Vita for building the application, which was a nice addition to my pretty lacking knowledge of JavaScript frameworks.

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All in all this was a fun project, I definitely learned a lot I can use in the future. Seeing how well OpenTripPlanner is, and not just for public transport but also walking and cycling, did give me a couple new ideas I could not envision in the past because I could only do it with Google's Routing API which would have been prohibitively expensive. Now I just need to start lobbying for the Bus Services Act 2017 or something similar to be implemented in Scotland as well

[callofdutymobileindia]

Best Weekend Data Science Courses in London for Working Professionals

Balancing a full-time job while wanting to upskill can be a tough ask. For professionals in London eager to make a shift into tech—or advance their current roles—weekend data science courses offer the perfect solution. They are designed to fit into busy schedules without compromising on content quality, ensuring you don’t have to choose between your career and your education.

In this comprehensive guide, we’ll take a deep dive into the best weekend data science courses in London for working professionals, what you should look for in a program, and how such courses can act as a stepping stone toward roles in Artificial Intelligence, Machine Learning, and beyond.

Why Weekend Learning Makes Sense for Busy Professionals

Weekend data science education addresses the practical realities of modern professional life. Most working adults cannot commit to weekday classes without significant career disruption. Weekend programs acknowledge this constraint while maintaining rigorous academic standards. Students typically find Saturday and Sunday sessions allow for deeper focus without workplace distractions.

The concentrated nature of weekend learning often enhances retention and practical application. Students can immediately apply newly acquired concepts to their current roles, creating a feedback loop that reinforces learning. This real-world application distinguishes weekend programs from theoretical academic approaches that may lack immediate relevance.

Top-Tier Weekend Data Science Programs in London

Imperial College London Executive Education

Imperial College's weekend data science program stands among Europe's most prestigious offerings. Their Saturday intensives combine theoretical rigor with practical application, taught by faculty who actively research machine learning and artificial intelligence. The program spans twelve weekends, covering everything from statistical foundations to advanced neural networks.

Students appreciate Imperial's emphasis on real-world case studies drawn from finance, healthcare, and technology sectors. The curriculum includes hands-on projects using Python, R, and specialized data visualization tools. Imperial's industry connections ensure course content remains current with market demands.

London School of Economics Weekend Analytics Programme

LSE's reputation for social science excellence extends naturally into data analytics education. Their weekend program uniquely emphasizes the intersection between data science and business strategy. Students learn not just technical skills but also how to communicate insights effectively to senior management.

The curriculum balances statistical theory with practical business applications. Students work with real datasets from LSE's research partnerships, gaining experience with messy, imperfect data that mirrors professional environments. The program particularly excels in teaching data ethics and privacy considerations—increasingly crucial topics in today's regulatory environment.

University College London Centre for Advanced Spatial Analysis

UCL's weekend offerings focus specifically on spatial data science and geographic information systems. While specialized, these skills prove increasingly valuable across sectors from urban planning to retail analytics. The program attracts professionals from consulting, government, and technology companies seeking location-based analytical capabilities.

Students learn to work with satellite imagery, GPS data, and demographic information using specialized software like ArcGIS and QGIS. The curriculum includes machine learning applications for spatial data, a rapidly growing field with applications in autonomous vehicles, smart cities, and environmental monitoring.

Specialized Artificial Intelligence Courses for Weekend Learners

The growing importance of artificial intelligence has spawned numerous weekend programs dedicated specifically to AI applications. These courses typically assume some technical background but remain accessible to motivated professionals from non-technical fields.

King's College London AI for Business Leaders

King's weekend AI program targets senior professionals who need to understand artificial intelligence's strategic implications without necessarily implementing solutions themselves. The curriculum covers machine learning fundamentals, natural language processing, and computer vision applications across various industries.

Students particularly value sessions on AI ethics and bias mitigation. The program includes case studies from healthcare, finance, and retail sectors, demonstrating how AI transforms traditional business models. Guest speakers from leading AI companies provide insights into emerging trends and technologies.

Birkbeck College Applied Machine Learning

Birkbeck's evening and weekend college heritage makes them naturally suited for working professional education. Their applied machine learning program runs alternate Saturdays over six months, providing time for concept absorption between sessions.

The hands-on approach emphasizes practical implementation over theoretical derivations. Students work with popular frameworks like TensorFlow and PyTorch, building neural networks for image recognition, text analysis, and predictive modeling. The curriculum includes deployment considerations, helping students understand how to move from prototype to production systems.

Intensive Bootcamp Options for Rapid Skill Development

Several organizations offer intensive weekend bootcamps designed for rapid skill acquisition. These programs trade depth for breadth, providing survey-level exposure to multiple data science disciplines.

General Assembly Weekend Data Science Circuit

General Assembly's weekend circuit compresses their full-time bootcamp into Saturday-Sunday sessions over three months. Students cover Python programming, statistical analysis, machine learning, and data visualization in rapid succession.

The pace demands significant between-session study, but motivated students acquire practical skills quickly. GA's industry connections facilitate job placement support, though most weekend students seek skill enhancement rather than career changes.

Makers Academy Weekend Intensive

Makers Academy applies their coding bootcamp methodology to data science education. Weekend sessions emphasize collaborative learning and problem-solving over lecture-based instruction.

Students work in pairs and small groups, mirroring professional development environments. The curriculum emphasizes software engineering practices often overlooked in academic data science programs—version control, testing, and code documentation.

Choosing the Right Program for Your Career Goals

Selecting appropriate weekend data science education requires careful consideration of several factors. Career objectives should drive program selection more than institutional prestige or convenience factors.

Professionals seeking broad analytical literacy might prefer survey courses covering multiple disciplines. Those with specific technical needs—spatial analysis, natural language processing, or computer vision—benefit from specialized programs despite narrower scope.

Maximizing Weekend Learning Success

Success in weekend data science programs demands different strategies than traditional education. Time management becomes crucial when balancing course requirements with professional responsibilities.

Advance preparation often determines success. Students benefit from reviewing prerequisites before courses begin, ensuring they can focus on new concepts rather than catching up on fundamentals. Many programs provide recommended reading lists or online resources for advance study.

Active participation enhances learning outcomes significantly. Weekend formats typically encourage discussion and collaborative problem-solving. Students who engage actively with instructors and peers report better comprehension and stronger professional networks.

Financial Considerations and Return on Investment

Weekend data science education represents a significant financial investment, with quality programs ranging from £2,000 to £15,000 depending on duration and institutional prestige. However, the return on investment often justifies the expense through salary increases, promotions, or career transitions.

Many employers support professional development through training budgets or tuition reimbursement programs. Discussing weekend education plans with supervisors often reveals available funding sources while demonstrating initiative and commitment to professional growth.

Building Professional Networks Through Weekend Learning

Weekend data science programs attract motivated professionals from diverse industries, creating unique networking opportunities. Unlike undergraduate programs with homogeneous student bodies, weekend cohorts bring varied perspectives and experiences.

Class projects often benefit from this diversity, as students contribute domain expertise from their respective fields. Finance professionals understand regulatory constraints, healthcare workers grasp clinical workflows, and technology professionals appreciate implementation challenges.

The Future of Weekend Data Science Education

Weekend data science education continues evolving in response to technological advances and changing professional needs. Hybrid learning models combining in-person and online elements provide greater flexibility while maintaining interpersonal connections.

Emerging technologies like virtual reality and augmented reality promise enhanced learning experiences, particularly for complex visualization concepts. Interactive simulations may eventually replace traditional lecture formats, allowing students to experiment with algorithms and datasets in immersive environments.

Micro-credentials and digital badges increasingly provide granular skill verification, allowing professionals to demonstrate specific competencies rather than broad program completion. This trend particularly benefits weekend learners who may need targeted skills rather than comprehensive education.

Final Thoughts

Final Thoughts

If you’re a working professional in London, there’s never been a better time to invest in your future with a weekend data science course. The flexibility, practical learning, and career opportunities make it an ideal choice for those juggling work and ambition.

Whether you’re looking to gain a competitive edge in your current field or switch entirely to a data-driven role, the Data Science Courses in London listed above offer high-quality training tailored for busy professionals.

And if you also want to explore cutting-edge technologies like AI, consider enrolling in a program that combines both fields. The Artificial Intelligence Course in London offered by Boston Institute of Analytics is a stellar example—equipping you with skills that are not just relevant today, but also tomorrow.

[distinguishedconnoisseurgarden]

QGIS Course: Unlock the Power of Open-Source GIS Software

Geographic Information System (GIS) has revolutionized the way we analyze spatial data, and QGIS stands as one of the most powerful open-source GIS software available today. Whether you are a beginner or an advanced user, enrolling in a QGIS course can significantly enhance your GIS skills. In this blog, we’ll explore the benefits of learning QGIS, what a typical course covers, and why it’s a must-have skill in various industries.

Why Learn QGIS?

QGIS is widely used for mapping, spatial analysis, and data visualization. Here’s why learning QGIS can be a game-changer for your career:

Open-Source & Free – Unlike other GIS software, QGIS is completely free, making it accessible to anyone.

User-Friendly Interface – Intuitive design and easy-to-use tools make it ideal for beginners.

Wide Industry Applications – From environmental science and urban planning to agriculture and disaster management, QGIS is utilized across various sectors.

Integration with Other Tools – QGIS supports various plugins and integrates with other GIS tools, enhancing its functionality.

What You Will Learn in a QGIS Course

A comprehensive QGIS course typically covers:

Introduction to GIS & QGIS – Basics of GIS, spatial data types, and an overview of QGIS.

Data Handling & Management – Importing, managing, and analyzing spatial datasets.

Cartography & Mapping – Creating maps with customized symbology and layouts.

Spatial Analysis – Buffering, overlay analysis, and geoprocessing techniques.

Advanced Tools & Plugins – Automation with Python (PyQGIS), 3D mapping, and raster data processing.

Real-World Projects – Hands-on exercises to apply learned skills in practical scenarios.

Who Should Enroll?

QGIS courses are beneficial for:

GIS professionals looking to enhance their expertise.

Researchers and students working with spatial data.

Urban planners, environmentalists, and government agencies.

Anyone interested in geospatial analysis and mapping.

Pie Chart: Industries Using QGIS

Environmental Service and Conservation: 30%

Urban & Regional Planning: 25%

Agriculture &  Forestry: 15%

Disaster Management: 10%

Transportation & Logistics: 10%

Others (Education, Health, etc.): 10%

Conclusion:

A QGIS course provides you with essential skills to navigate and manage GIS projects effectively. Whether you're a student aiming to enhance your academic research, a professional seeking to advance your career, or a GIS enthusiast exploring new opportunities, mastering QGIS can be a game-changer. This open-source software empowers you to visualize, analyze, and interpret spatial data with ease.

At BIMMANTRA Academy, we offer a comprehensive QGIS course designed to provide hands-on experience in geospatial mapping, data manipulation, and advanced GIS techniques. Our expert-led training ensures that you gain practical knowledge applicable to real-world GIS projects.

Don't miss the chance to upgrade your skill set—enroll with BIMMANTRA Academy today and unlock the full potential of geospatial technology.

[davidjohnstongisschool]

Cartography and Mapping: Master the Art of Map Design

Maps have been an essential part of human civilization, guiding explorers, connecting societies, and visualizing the world. Cartography, the science and art of creating maps, is more than just plotting geographic data—it’s a creative process that combines precision, design, and storytelling. Whether you’re a seasoned professional or a curious novice, mastering the art of map design is an exciting journey into the world of cartography and mapping.

The Evolution of Cartography and Mapping

The history of cartography is as ancient as humanity’s curiosity about the world. From prehistoric cave paintings to sophisticated digital maps, the evolution of cartography reflects advancements in technology, science, and human understanding.

Early Maps:

The earliest known maps were etched on clay tablets or carved into rocks, representing local geography or celestial observations.

Ancient civilizations like the Babylonians, Greeks, and Chinese contributed to cartography, creating maps for navigation, agriculture, and astronomy.

Medieval Maps:

During the Middle Ages, maps were often symbolic, combining geographic information with religious or cultural narratives.

The creation of the mappa mundi (world maps) illustrated the medieval worldview, often centering Jerusalem or religious landmarks.

The Age of Exploration:

The 15th and 16th centuries marked significant progress, fueled by exploration and trade.

Cartographers like Gerardus Mercator developed projection systems to depict the spherical Earth on flat surfaces, revolutionizing navigation.

Modern and Digital Mapping:

The 20th and 21st centuries saw the emergence of aerial photography, satellite imagery, and Geographic Information Systems (GIS).

Today, maps are dynamic, interactive, and accessible through platforms like Google Maps, blending real-time data with advanced visualization techniques.

Principles of Effective Map Design

Designing a compelling map requires a balance of aesthetics, clarity, and functionality. Here are some essential principles to master the art of map design:

Purpose and Audience:

Define the purpose of your map. Is it for navigation, education, or storytelling?

Understand your audience’s needs and preferences to ensure the map is relevant and user-friendly.

Data Selection and Simplification:

Choose the right data for your map, focusing on what’s most relevant to the purpose.

Simplify complex data without sacrificing accuracy to enhance readability.

Map Elements:

Title: A concise, descriptive title sets the context for the map.

Legend: Provide a clear legend to explain symbols, colors, and scales.

Scale and North Arrow: Indicate distances and orientation for navigation.

Labels: Use consistent, legible typography for place names and annotations.

Color and Symbolism:

Use color to differentiate features, convey meaning, and create visual appeal.

Consider colorblind-friendly palettes to ensure accessibility.

Projection and Scale:

Choose an appropriate projection to minimize distortion based on the map’s purpose.

Select a scale that balances detail and coverage.

Hierarchy and Focus:

Emphasize key elements using size, color, or placement.

Maintain a visual hierarchy to guide the viewer’s attention.

Tools and Technologies in Modern Cartography

Modern cartography and mapping are powered by advanced tools and technologies, making it easier to create precise and engaging maps. Here are some popular tools for map design:

GIS Software:

Tools like ArcGIS, QGIS, and Mapbox allow users to analyze spatial data, create detailed maps, and visualize patterns.

Design Software:

Adobe Illustrator and Affinity Designer are ideal for customizing maps and adding artistic elements.

Online Mapping Platforms:

Platforms like Google Maps API and OpenStreetMap provide interactive mapping solutions for web applications.

Programming Languages:

Python, R, and JavaScript are commonly used for automating map creation, data analysis, and interactive design.

Remote Sensing and Drones:

Satellite imagery and drone technology enable high-resolution mapping and real-time updates.

Cartography as an Art Form

While cartography is rooted in science, it is also an artistic endeavor that blends creativity with technical skills. A beautifully designed map can tell a story, evoke emotions, and inspire curiosity. Here are some ways to embrace the artistic side of cartography:

Creative Map Styles:

Experiment with artistic styles, such as watercolor maps, vintage designs, or minimalist aesthetics.

Use thematic maps to highlight specific topics, like population density, climate change, or historical events.

Typography and Labeling:

Choose fonts that complement the map’s style and enhance readability.

Pay attention to label placement to avoid clutter and confusion.

Inspiration from Nature:

Study natural landscapes for inspiration, mimicking the textures, colors, and patterns found in the environment.

Storytelling Through Maps:

Combine maps with narratives, images, or interactive elements to create immersive experiences.

The Future of Cartography and Mapping

The field of cartography is constantly evolving, shaped by emerging technologies and changing societal needs. Here are some trends shaping the future of mapping:

Interactive and Immersive Maps:

Virtual Reality (VR) and Augmented Reality (AR) are transforming how we interact with maps, offering immersive experiences for navigation and exploration.

Big Data and AI:

Artificial intelligence and big data analytics are enhancing map accuracy, predictive modeling, and decision-making.

Sustainability and Environmental Mapping:

Maps are playing a crucial role in addressing environmental challenges, from tracking deforestation to monitoring climate change.

Personalized and Adaptive Maps:

Advances in machine learning enable maps to adapt to individual user preferences, providing tailored experiences.

Open Data and Collaboration:

The rise of open data initiatives and collaborative mapping projects, like OpenStreetMap, is democratizing access to geographic information.

Conclusion

Cartography and mapping are dynamic fields that combine science, art, and technology to represent and explore the world. By mastering the principles of map design, leveraging modern tools, and embracing creativity, you can create maps that inform, inspire, and engage. Whether you’re crafting a simple road map or an intricate thematic representation, the journey of mastering cartography is as rewarding as the maps themselves. So, pick up your tools, and start designing maps that leave a lasting impact on your audience.

[paulward]

The Best GIS Software Tools: Empowering Your Spatial Projects

Geographic Information Systems (GIS) have transformed how we understand, analyze, and visualize spatial data. GIS software tools offer robust capabilities for mapping, analyzing patterns, and making data-driven decisions across sectors like urban planning, environmental science, business, and more. With various GIS software tools available, selecting the right one for your project can be overwhelming. Here’s a rundown of some of the best GIS software tools that empower your spatial projects, helping you make informed choices based on project needs, budget, and technical expertise.

1. ArcGIS – A Comprehensive GIS Solution

ArcGIS, developed by Esri, is one of the most powerful and widely used GIS software tools in the industry. Known for its extensive functionalities, ArcGIS offers tools for mapping, spatial analysis, data management, and visualization, making it suitable for professionals and organizations of all sizes.

Key Features:

3D Visualization: ArcGIS allows users to create and manipulate 3D models, a feature essential for projects requiring depth and topographic insights.

Data Interoperability: Supports various data formats, enabling users to integrate data from multiple sources seamlessly.

Geostatistical Analysis: Tools for advanced statistical analysis and geostatistical modeling allow for deep insights into spatial trends and patterns.

Cloud-Based Options: ArcGIS Online facilitates cloud storage, allowing teams to collaborate on projects remotely and securely.

Use Cases: Ideal for urban planners, government agencies, and environmental organizations that need powerful, flexible, and scalable GIS software for large-scale analysis and mapping.

2. QGIS – The Leading Open-Source GIS Tool

Quantum GIS, or QGIS, is an open-source GIS software that has gained popularity for its flexibility and cost-effectiveness. As a free software solution, it allows users of all experience levels to access advanced GIS tools and contribute to its continuous development.

Key Features:

Customizable Plugins: QGIS supports a vast library of plugins that extend its capabilities for specific project needs.

User-Friendly Interface: While QGIS has powerful features, it also offers a straightforward interface, making it accessible for beginners.

Cross-Platform Support: Available for Windows, macOS, and Linux, QGIS is versatile across different operating systems.

Python Scripting: Allows users to automate workflows and customize analysis using Python scripts.

Use Cases: QGIS is ideal for NGOs, researchers, and educators looking for a robust yet budget-friendly GIS solution. It’s also suitable for companies aiming to avoid licensing fees while maintaining analytical rigor.

3. MapInfo Pro – Targeted GIS for Business Analysis

MapInfo Pro, developed by Pitney Bowes, is a GIS software tool known for its spatial data visualization and business analytics capabilities. With a focus on location intelligence, MapInfo Pro helps users make decisions by visualizing customer locations, supply chains, and service areas.

Key Features:

Spatial Analytics: MapInfo Pro provides powerful tools for spatial data analysis, such as heat mapping and proximity analysis.

Data Integration: Supports integration with other business intelligence (BI) tools, making it ideal for organizations with existing data infrastructure.

Industry-Specific Solutions: Offers add-ons tailored to retail, banking, insurance, and telecommunications sectors.

Customizable Workspace: Users can customize the workspace to focus on specific data visualization needs, streamlining workflows.

Use Cases: MapInfo Pro is highly effective for companies in retail, banking, and insurance industries where geographic patterns impact customer behavior and operational efficiency.

4. Google Earth Engine – Cloud-Based GIS for Large-Scale Environmental Analysis

Google Earth Engine (GEE) is a cloud-based GIS software tool tailored for environmental and sustainability projects. It leverages Google’s cloud infrastructure to store and analyze petabytes of satellite imagery, enabling complex analysis on a global scale.

Key Features:

Remote Sensing and Imagery Processing: Access to a vast library of satellite data allows users to conduct time-series analysis and environmental monitoring.

Scalability: With cloud computing, GEE can process large datasets, making it suitable for projects that require real-time analysis of global datasets.

API Access: Programmers can interact with GEE through APIs, enabling integration with other applications and advanced customization.

Data Sharing: Collaborative tools allow researchers and organizations to share findings and analysis, promoting global knowledge sharing.

Use Cases: Perfect for environmental agencies, researchers, and NGOs conducting projects on deforestation, climate change, and land use monitoring. GEE’s strength in handling massive datasets makes it valuable for global-scale analyses.

5. Global Mapper – A Cost-Effective All-Rounder

Global Mapper by Blue Marble Geographics is an affordable GIS software tool with capabilities in data analysis, 3D modeling, and mapping. It’s often chosen by users who need a broad set of GIS functions without the steep cost associated with enterprise-level solutions.

Key Features:

Data Visualization: Comprehensive data visualization tools include contour mapping and terrain analysis.

Built-In Digitizing Tools: Users can create and edit vector data within the application, saving time on data preparation.

3D Modeling: Global Mapper provides robust 3D visualization and analysis, ideal for projects with elevation data.

Customizable Interface: The software’s interface can be tailored to the user's needs, simplifying workflows for specific tasks.

Use Cases: Global Mapper is ideal for smaller engineering firms, environmental consultants, and independent GIS analysts looking for a well-rounded, affordable solution.

6. GeoDa – A Statistical Focus for Spatial Data

GeoDa is an open-source GIS software tool specifically designed for spatial data analysis. It focuses on statistical analysis, making it popular in academia and research sectors where the focus is on analyzing spatial relationships and patterns.

Key Features:

Exploratory Spatial Data Analysis (ESDA): GeoDa’s focus on ESDA provides a unique advantage for researchers exploring spatial patterns.

Advanced Statistical Tools: Users can conduct regression analysis and clustering, helpful for understanding data distributions and relationships.

User-Friendly Interface: GeoDa’s clean, intuitive interface makes it easy for new users to perform complex statistical operations.

Interactive Visualization: Dynamic maps and graphs help visualize spatial relationships and statistical outputs.

Use Cases: GeoDa is highly useful in academia, especially in fields like epidemiology, public health, and economics, where spatial relationships are crucial to research findings.

7. GRASS GIS – A High-Performance Open-Source Option for Geospatial Analysis

GRASS GIS, or Geographic Resources Analysis Support System, is a powerful open-source GIS tool that offers advanced geospatial modeling, spatial data management, and environmental modeling capabilities. It’s particularly known for its performance in handling large datasets.

Key Features:

Robust Data Management: GRASS GIS excels in managing and processing raster, vector, and 3D data, with options for detailed customization.

Environmental Modeling: Its suite of tools for environmental analysis, hydrology, and landscape ecology makes it a go-to for environmental scientists.

Script-Based Analysis: Supports both Python and shell scripting for automation and complex analysis.

Community Support: As an open-source tool, GRASS GIS benefits from a global community of contributors, regularly improving its functionalities.

Use Cases: GRASS GIS is well-suited for environmental agencies, researchers, and GIS professionals focused on projects like land management, ecological modeling, and urban planning.

8. ERDAS IMAGINE – Advanced Remote Sensing Capabilities

ERDAS IMAGINE is a powerful GIS tool for remote sensing, particularly popular in the fields of satellite and aerial image analysis. Its image-processing capabilities make it a preferred choice for professionals working with raster data.

Key Features:

Advanced Image Processing: ERDAS IMAGINE excels in processing multispectral, hyperspectral, and radar imagery.

Data Fusion: Users can combine multiple data sources for enhanced image analysis, crucial for detailed land classification and pattern recognition.

Geoprocessing Tools: Offers extensive geoprocessing capabilities for topographic analysis, change detection, and land cover classification.

3D Visualization: Allows users to create detailed 3D representations, useful for environmental analysis and planning.

Use Cases: This software is highly suited for government agencies, defense departments, and research institutions that require in-depth satellite image analysis.

Selecting the Right GIS Tool for Your Needs

Choosing the best GIS software tool depends on factors like project scope, budget, technical requirements, and team expertise. Each tool has unique strengths, whether it’s ArcGIS for comprehensive spatial analysis, QGIS for a cost-effective solution, or Google Earth Engine for large-scale environmental monitoring. By aligning the tool's capabilities with your project goals, you’ll maximize efficiency and gain deeper insights from spatial data.

GIS software tools have revolutionized how industries operate, making spatial analysis more accessible, efficient, and impactful. Embrace these tools to empower your spatial projects and unlock the full potential of geographic data.

[geone-ws]

Using #Python in #QGIS to scale up London's cycle infrastructure to match Amsterdam.

https://medium.com/@eamonmagd/using-python-in-qgis-to-scale-up-londons-cycle-infrastructure-to-match-amsterdam-c518cb0218de

[kentrix]

Essential Geospatial Data Analysis Tools

Geospatial data analysis is a crucial aspect of numerous fields such as environmental science, urban planning, and geography. Various tools facilitate the collection, processing, analysis, and visualization of geospatial data. Here are some essential geospatial data analysis tools:

Geographic Information Systems (GIS) Software

ArcGIS: Developed by Esri, ArcGIS is a comprehensive and widely-used GIS software. It offers robust tools for mapping, spatial analysis, and data management. ArcGIS includes desktop applications (ArcMap, ArcGIS Pro), web-based solutions, and mobile apps.

QGIS: An open-source GIS software, QGIS provides a wide range of features for spatial data visualization, editing, and analysis. It’s highly extensible with plugins and supports various data formats.

GRASS GIS: Another powerful open-source GIS, GRASS (Geographic Resources Analysis Support System) GIS is known for its advanced geospatial data management and analysis capabilities, especially in raster data processing.

Remote Sensing Tools

ENVI: A software platform for processing and analyzing geospatial imagery. ENVI is particularly useful for remote sensing applications and supports a wide array of data formats from various sensors.

Google Earth Engine: A cloud-based platform for planetary-scale environmental data analysis. Google Earth Engine provides access to a vast archive of satellite imagery and geospatial datasets and is widely used for environmental monitoring and research.

Data Analysis and Visualization Tools

GDAL (Geospatial Data Abstraction Library): An open-source library for reading and writing raster and vector geospatial data formats. GDAL is a fundamental tool for data translation and processing.

R (with spatial packages like rgdal, sp, raster): R is a programming language and environment commonly used for statistical computing and graphics. With packages like rgdal, sp, and raster, R becomes a powerful tool for geospatial data analysis and visualization.

Python (with libraries like GeoPandas, Shapely, Rasterio): Python is widely used in geospatial analysis due to its readability and extensive libraries. GeoPandas simplifies working with geospatial data, Shapely provides tools for geometric operations, and Rasterio is used for raster data manipulation.

Also Read: Understanding Consumer Behavior Trends in 2024

Web Mapping Tools

Leaflet: An open-source JavaScript library for interactive maps. Leaflet is lightweight and mobile-friendly, making it a popular choice for web mapping applications.

Mapbox: A platform for designing and publishing custom maps. Mapbox offers robust APIs and SDKs for integrating maps into web and mobile applications.

Database Management Systems

PostGIS: An extension of the PostgreSQL database, PostGIS adds support for geographic objects. It allows spatial queries to be run in SQL and is highly efficient for managing large spatial datasets.

Spatialite: An extension of the SQLite database, Spatialite provides support for spatial data. It’s lightweight and useful for applications where a full-scale spatial database like PostGIS isn’t necessary.

Workflow and Automation Tools

FME (Feature Manipulation Engine): A tool for data integration and automation, FME supports a vast range of data formats and helps automate the transformation and movement of geospatial data.

Kepler.gl: An open-source tool developed by Uber for large-scale geospatial data visualization. Kepler.gl is user-friendly and excels in handling and visualizing large datasets interactively.

These tools cover a broad spectrum of needs in geospatial data analysis, from data management and processing to advanced spatial analysis and interactive visualization. The choice of tools often depends on the specific requirements of the task, the scale of the data, and user expertise.

[ai-news]

#AI #ML #Automation

[rfitrianingtyas]

Tahun baru, semester baru, waktu yang tepat rasanya untuk memikirkan rencana-rencana baik ke depan. Sebenarnya, Ga melulu tentang rencana, ada beberapa kebiasaan yang ingin saya ubah di keseharian 24/7. Saya pernah membaca, daripada sibuk memikirkan resolusi yang berfokus pada hasil, bagaimana jika kita fokus ke prosesnya saja. Misal, di tahun ini saya ingin bisa publish di salah satu jurnal internasional terakreditasi. Bagaimana caranya? Rutin satu hari minimal baca 1 jurnal, konsisten menulis, dan perbaiki komunikasi untuk membangun networking.

Baik, termotivasi dari tulisan itu saya coba jabarkan apa saja rencana dan perbaikan untuk satu semester ke depan:

1. Niatkan dalam melakukan setiap kebaikan untuk dapat ridho Allah SWT dan tidak mengharapkan apapun setelahnya dari manusia, baik berupa pengakuan maupun timbal balik serupa.

2. Kurang-kurangin ovt, alihkan dengan menulis. Ada ide, Tulis! Ada keresahan, tulis! Mengalami kebuntuan, Tulis!

3. Perbaiki komunikasi. Jangan ragu menyapa meskipun dengan seseorang yg kurang akrab sekalipun, bahkan tahu sepertinya orang tersebut tidak ingat denganmu. Dan jangan segan meminta tolong! Terkadang bonding terbentuk karena perasaan saling membutuhkan.

4. Melatih softskill dengan cara: berlatih bahasa asing dan mengambil sertifikasi (sebelum bulan Februari 2024 berakhir), mengambil course bahasa pemrograman Python dengan topik ML (sebelum Bulan Juli 2024), belajar software perpetaan QGis (di Bulan Januari 2024)

5. Menulis 1 jurnal terakreditasi sebagai penulis pertama minimal Sinta 2 dan ikut berpartisipasi menulis 1 jurnal internasional terindeks Scopus.

Cukup, semoga Allah mampukan. Aamiin

[aksitaniladewanti]

Analis Penelitian Data Skripsi Tsesi Diserta Statistika, Amos Python SmartPLS RSTudio SEM Arcgis Qgis

[myjondaleh]

Mastering QGIS Desktop 3.34 LTR: The Ultimate GIS Guide.

English Video tutorial, just for $9.99 (NORMAL price $19.9). Get it once and you will get endless access right.

Master QGIS Desktop 3.34 LTR with this step-by-step guide designed for GIS professionals and enthusiasts. Learn essential workflows, advanced mapping techniques, geoprocessing tools, and automation for powerful spatial data management. Elevate your GIS expertise today!

[disastergis]

Three maps from the Palisades fire in Los Angeles City.

The 1st one shows the WiFire model and aircraft fighting the fire

The 2nd one shows the Evacuation areas, but pay close attention to the inset boxes on the right which shows the OFFICIAL VERSIONS/Tweets put out to let residents know. Seriously... that happened. They used a cell phone pic of a paper map. 

The third one shows the fire perimeter, updated evac zones and more. 

[geoafrikana]

What skills do I need as a #RemoteSensing Expert?* That's a bit more complicated question to answer, but let's try to put everything in place. A remote sensing expert is a person who is able to perform processing methodologies on data (mainly images and in-situ measurements) and extract meaningful information from these data in order to feed #business (and technical) decisions. *That said, let's try to make it a bit more specific:* 🌹- #Mathematics and linear #algebra 🌹- A Programming language like R or #Python (imagine it as a tool assist you, not as the purpose) 🌹- Specific remote sensing software (ENVI, ERDAS, ORFEO, SNAP, #QGIS, GRASS, etc) 🌹- Understanding of the domain specific you want to work for (e.g. #Agriculture) https://www.instagram.com/p/B6vtYyfhrA4/?igshid=8v2979ll1y9w

[quaranmine]

you'd think it'd be easier to figure this out but most of what i'm getting is like "go to the catalog portal and add data from your organization or the web!" like yeah if i wanted random california wildfire data from 2016 that might help BUT THE DATA I NEED TO MAP DOESNT EXIST ONLINE BECAUSE IT'S MINE, NOT SOMEONE ELSE'S

fully aware i am probably just blatantly missing some tutorial that does tell me exactly that but i haven't found it yet lol

stumbling my way through arcgis pro by googling the absolute most basic questions like "how do i add a new layer"

[davidjohnstongisschool]

Top Tools and Techniques for Effective Geospatial Analysis

Geospatial analysis is the process of examining geographic data to uncover patterns, relationships, and trends. It plays a pivotal role across industries such as urban planning, environmental management, transportation, and marketing. By leveraging geospatial analysis, businesses and governments can make informed decisions backed by spatial insights.

In this blog, we’ll explore the top tools and techniques for effective geospatial analysis. Whether you're a beginner or an experienced professional, this guide will help you choose the right resources to enhance your geospatial capabilities.

What is Geospatial Analysis?

Geospatial analysis involves processing and analyzing geographic data—data tied to a specific location. This could be coordinates, addresses, or geographic boundaries. By visualizing and interpreting this data, organizations can:

Identify spatial patterns and trends.

Solve location-based challenges.

Make data-driven decisions.

Key Tools for Geospatial Analysis

1. Geographic Information Systems (GIS)

GIS tools are foundational for geospatial analysis. They allow users to visualize, manage, and analyze geographic data in layers, offering insights into spatial relationships.

Popular GIS Tools:

ArcGIS: A comprehensive platform by Esri, ArcGIS is widely used for mapping, spatial analysis, and data visualization. Its robust features include 3D mapping, predictive modeling, and integration with big data.

QGIS: An open-source alternative to ArcGIS, QGIS is free and highly customizable. It supports plugins for advanced spatial analysis and is ideal for those on a budget.

MapInfo Pro: Known for its user-friendly interface, MapInfo Pro is often used for business applications like market analysis and asset management.

2. Remote Sensing Software

Remote sensing tools analyze satellite or aerial imagery to gather data about the Earth's surface. This is particularly useful for environmental monitoring, agriculture, and urban development.

Popular Remote Sensing Tools:

Google Earth Engine: A cloud-based platform for analyzing geospatial data on a massive scale. It's widely used for environmental research and disaster response.

ENVI: Ideal for processing satellite and aerial imagery, ENVI offers advanced capabilities like spectral analysis and machine learning integration.

3. Spatial Data Visualization Tools

Visualization tools transform raw spatial data into intuitive maps and graphics, making it easier to communicate findings.

Popular Visualization Tools:

Tableau: Known for its ease of use, Tableau integrates geospatial data to create interactive visualizations.

Kepler.gl: An open-source tool designed for large-scale geospatial data visualization, offering features like heatmaps and time-based animations.

Carto: A cloud-based platform for spatial data analysis and visualization, Carto is particularly popular for business intelligence applications.

4. Programming Languages for Spatial Analysis

Programming languages like Python and R have libraries and packages specifically designed for geospatial analysis.

Key Libraries and Frameworks:

Python (Geopandas, Shapely, Folium, PySAL): Python’s libraries enable everything from data manipulation to advanced spatial statistics and web mapping.

R (sf, raster, tmap): R offers powerful tools for statistical modeling and spatial data visualization.

5. Geospatial Databases

Geospatial databases store and query spatial data efficiently. They are essential for handling large datasets and ensuring seamless analysis.

Popular Geospatial Databases:

PostGIS: An extension of PostgreSQL, PostGIS is widely used for storing and querying geographic objects.

SpatiaLite: A lightweight alternative to PostGIS, ideal for smaller projects.

MongoDB with GeoJSON: MongoDB supports geospatial indexing and queries using GeoJSON, making it suitable for web-based applications.

Top Techniques for Geospatial Analysis

1. Spatial Interpolation

Spatial interpolation estimates values at unsampled locations based on known data points. It’s widely used in meteorology, agriculture, and environmental science.

Common Methods:

Kriging

Inverse Distance Weighting (IDW)

Spline Interpolation

2. Hot Spot Analysis

Hot spot analysis identifies areas of statistically significant clustering. This is especially useful in crime analysis, disease tracking, and market research.

Tools for Hot Spot Analysis:

ArcGIS Spatial Statistics Toolbox

Python's PySAL library

3. Network Analysis

Network analysis examines the connectivity and flow within spatial networks, such as transportation systems or utility grids.

Applications:

Shortest path analysis for navigation systems.

Optimization of delivery routes for logistics.

Analysis of infrastructure vulnerability.

4. Change Detection

Change detection identifies differences in a location over time, often using remote sensing imagery.

Applications:

Monitoring deforestation and urban sprawl.

Assessing disaster impacts.

Analyzing agricultural productivity.

5. Geocoding and Reverse Geocoding

Geocoding converts addresses into geographic coordinates, while reverse geocoding converts coordinates into readable addresses.

Popular Tools:

Google Maps API

ArcGIS Geocoding Service

6. Spatial Regression

Spatial regression analyzes relationships between variables in a geographic context. It’s used in fields like economics, epidemiology, and environmental science.

Key Methods:

Ordinary Least Squares (OLS)

Geographically Weighted Regression (GWR)

7. Cluster Analysis

Cluster analysis groups spatial data points based on similarities. It’s useful for market segmentation, crime mapping, and disease outbreak analysis.

Common Techniques:

K-Means Clustering

DBSCAN

Hierarchical Clustering

Emerging Trends in Geospatial Analysis

AI and Machine Learning Integration Advanced algorithms are enhancing geospatial analysis, enabling predictive modeling and pattern recognition. For instance, machine learning models can classify satellite imagery or predict traffic flow.

Cloud-Based Geospatial Platforms Cloud computing enables the analysis of massive geospatial datasets without local hardware constraints. Platforms like Google Earth Engine and AWS provide scalable solutions.

3D and Real-Time Mapping 3D mapping tools like Esri's ArcGIS Pro offer realistic visualizations, while real-time data feeds enable dynamic mapping for applications like traffic monitoring.

Augmented Reality (AR) and Virtual Reality (VR) AR and VR are being used to create immersive geospatial experiences, such as virtual tours of urban developments or disaster simulations.

IoT and Geospatial Analysis Internet of Things (IoT) devices generate real-time spatial data, which can be analyzed for applications like smart city planning and environmental monitoring.

How to Choose the Right Tools and Techniques

The choice of tools and techniques depends on your project goals, data availability, and budget. Here are some tips:

Define Your Objectives: Clearly outline what you aim to achieve with geospatial analysis.

Assess Your Data: Determine the type, format, and volume of data you'll be working with.

Consider Scalability: For large-scale projects, opt for cloud-based tools or databases with high processing power.

Budget Constraints: Open-source tools like QGIS and Kepler.gl are great for cost-effective analysis.

Conclusion

Geospatial analysis is transforming the way we understand and interact with the world. With powerful tools like GIS software, remote sensing platforms, and programming libraries, combined with advanced techniques such as hot spot analysis and machine learning, the possibilities are endless. By selecting the right tools and applying the appropriate techniques, you can unlock the full potential of geospatial data and drive impactful decisions.

Start exploring the world of geospatial analysis today to stay ahead in this data-driven era!