Spatio-temporal Trend Modeling of Environmental Data

Focused View

Seemab Akhtar

34:33

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1. Chapter 1

1. Introduction.mp4

01:16

2. Theory

1.1 The MannKendall Test.pdf

1. Theory.mp4

05:14

3. Pixel-to-Pixel Time Trend Modeling

1.1 Code pixel pixel trend.txt

1.2 data.zip

1. Pixel-to-Pixel Time Trend Modeling & Sens Slope.mp4

18:10

2.1 trend results.zip

2. Pixel-to-Pixel Trend Modeling Results.mp4

09:53

Description

Pixel-to-Pixel Time Trend Modeling, Graphical Representation and Cross-Validation

What You'll Learn?

Learn the methodology of applying the Mann-Kendall Test at the pixel level to identify monotonic trends in spatial data.
Grasp the theoretical foundations of the Mann-Kendall (M.K.) non-parametric test, Learn the intricacies of calculating the M.K. test statistics.
Use color gradients and other visual cues to represent trend magnitude and direction effectively.
Understand the significance and application of pixel-to-pixel time trend modeling.

Who is this for?

Environmental Scientists: Environmental scientists aiming to deepen their understanding of spatial trend analysis for environmental variables such as temperature, precipitation, and vegetation indices will find practical applications in their field. Geospatial Professionals: Professionals working in geospatial technology and GIS (Geographic Information System) can enhance their skill set with a focus on pixel-to-pixel time trend modeling, adding a valuable dimension to their geospatial analysis toolkit. Renewable Energy Researchers: Individuals conducting research in the field of renewable energy, especially those involved in geothermal energy exploration, will gain insights into spatial trend detection and optimization for heat extraction. Data Science Enthusiasts: Those interested in data science and analytics, seeking to apply statistical methods to spatial datasets, will benefit from the course's emphasis on the Mann-Kendall test and its applications in trend analysis. Graduate Students in Geosciences: Graduate students pursuing degrees in geosciences, including but not limited to geology, hydrogeology, and remote sensing, can supplement their academic knowledge with practical skills in spatial data modeling. Urban Planning Professionals: Urban planners looking to integrate geospatial analysis for understanding trends in urban areas, especially related to district heating and energy planning, will find the course content aligning with their professional objectives. This course is designed to accommodate a broad spectrum of learners, ensuring that individuals from various professional and academic backgrounds can acquire skills that are directly applicable to their specific areas of interest and expertise.

What You Need to Know?

Proficiency in R and RStudio: Learners should have a basic understanding of the R programming language and be familiar with RStudio for executing code and analyses. QGIS Familiarity: Prior exposure to QGIS (Quantum GIS) is beneficial but not mandatory. The course will cover relevant concepts, but basic familiarity with GIS tools is advantageous. Computer System with Minimum Specifications: Access to a laptop or PC with a minimum of 4GB RAM is essential to ensure a smooth learning experience and seamless execution of software tools. Dedication to Learning: An enthusiastic and dedicated approach to learning is encouraged. While no prior specific experience is required, a commitment to mastering the content will enhance the learning journey.

More details

Description
A Comprehensive Journey into Mann-Kendall Time Trend Modeling with R and QGIS. Uncover the essence of Mann-Kendall time trend modeling and its applications in diverse fields such as climate studies, hydrology, and environmental analysis. Gain proficiency in detecting and quantifying trends at the pixel level, addressing spatial autocorrelation with finesse.
Embark on a transformative learning experience tailored for research scholars, business analytics professionals, and engineering enthusiasts. Dive into the dynamic world of Mann-Kendall time trend modeling, gaining proficiency in R, RStudio, and QGIS. Equip yourself with essential skills and unleash the potential of data visualization, exploring temporal trends with clarity and precision.
Course Highlights:
Master R and RStudio for Statistical Mann-Kendall Analysis
Harness the Potential of QGIS for Spatial Insights
Visualize Temporal Trends Using Mann-Kendall Time Trend Modeling on Raster Data
Gain Practical Knowledge in Graphical Representation of Tabular Data
Build a Strong Foundation for Data-Driven Decision-Making
Engage in Hands-On Exercises and Real-World Applications
Immerse yourself in dynamic exercises translating theoretical concepts into practical insights. Engage with real-world scenarios, applying Mann-Kendall tests to authentic datasets, cultivating a data-driven decision-making mindset.
Enrol now and redefine your proficiency in decoding temporal trends within tabular and raster dataâ€”a skill that transcends boundaries and propels you into the echelons of data virtuosity.

Who this course is for:
Environmental Scientists: Environmental scientists aiming to deepen their understanding of spatial trend analysis for environmental variables such as temperature, precipitation, and vegetation indices will find practical applications in their field. Geospatial Professionals: Professionals working in geospatial technology and GIS (Geographic Information System) can enhance their skill set with a focus on pixel-to-pixel time trend modeling, adding a valuable dimension to their geospatial analysis toolkit. Renewable Energy Researchers: Individuals conducting research in the field of renewable energy, especially those involved in geothermal energy exploration, will gain insights into spatial trend detection and optimization for heat extraction. Data Science Enthusiasts: Those interested in data science and analytics, seeking to apply statistical methods to spatial datasets, will benefit from the course's emphasis on the Mann-Kendall test and its applications in trend analysis. Graduate Students in Geosciences: Graduate students pursuing degrees in geosciences, including but not limited to geology, hydrogeology, and remote sensing, can supplement their academic knowledge with practical skills in spatial data modeling. Urban Planning Professionals: Urban planners looking to integrate geospatial analysis for understanding trends in urban areas, especially related to district heating and energy planning, will find the course content aligning with their professional objectives. This course is designed to accommodate a broad spectrum of learners, ensuring that individuals from various professional and academic backgrounds can acquire skills that are directly applicable to their specific areas of interest and expertise.

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Geography

Seemab Akhtar

Instructor's Courses

I am a Hydrogeologist, recognized for my expertise in field hydrogeology, environmental monitoring, and impactful groundwater management. My versatile skill set encompasses Modflow modeling, assessments of groundwater abstraction, and the strategic design of boreholes. I specialize in assessing the influence of diverse activities—ranging from landfills, construction, mining, to agriculture—on groundwater quality and resource availability.Beyond my core in hydrogeology, I am a standout in Complete Geological/Orebody Modeling, employing advanced methodologies such as RBF. Proficient in navigating challenging terrains like BIF and Mylonite, I bring a wealth of experience from comprehensive geological field training in both igneous and sedimentary landscapes. My expertise extends to precise data collection techniques including Pitch, Dip, Strike Plunge, particularly in sedimentary and igneous terrains.I excel in various technical aspects, from groundwater level depth assessments, optical microscope slide-making, to soil testing and meticulous data cleaning. Well-versed in advanced geological processes, I am skilled in population modeling, compositing, capping, declustering, geological domaining, lithology correlation, contact analysis, and exploratory data analysis (EDA).My proficiency further includes various semi-variogram modeling techniques, utilizing cross-validation methods such as PKCV and LOOCV. I bring extensive experience in block selection (SMU), block model validation, grade-tonnage curve estimation, SGS, TBS, and Simulation Reduction.Driven by a commitment to excellence, my adeptness in a broad spectrum of geological and hydrogeological methodologies positions me as a valuable asset in the field.

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