Assignment3 (1)

3. Analyze, describe Algorithm ** 100% of the points : clear demonstration that you correctly used the terminologies, included relevant screen captures and explained all the steps 70% of the points: vague demonstration that you explained the steps or missed critical steps in explain the process of creating the map. 25% of the points: you incorrectly explained or missed most of the steps in explaining how the map was created step-by-step. 4. Design solutions to problems 100% of the points : a workflow you devised led to correct results and products you created (such as maps) help solve a problem at hand. 70% of the points: a workflow you devised led to correct results, but it is unclear that products you created (such as maps) help solve a problem at hand OR products you created could have helped solve a problem if a workflow you devised had led to correct results. 25% of the points: a workflow you devised led to incorrect results OR products you created (such as maps) does not help solve a problem at hand. MCD Learning Outcome-1: Computational thinking* Computational Thinking (CT) refers to abilities to formulate and solve problems in a way that can be computationally carried out . Most recurring CT concepts include: Note: Parts of MCD Learning Outcome represented in blue and underlined is evaluated in this assignment. 1. Decomposition (i.e. breaking down into smaller, manageable parts) Examples: Examples of decomposition is breaking down the main problem into smaller, manageable parts . For example, ‘creating a map’ includes, extracting and adding the spatial data, manipulating the spatial data to visually represent the data and add the visual elements to be able to read the data in map format by both technical and non-technical folks . 2. Abstraction (i.e., reducing a complex problem or reality to essential components) Examples: Examples of abstraction as it relates to GIS, are datum for unambiguously referencing locations on the Earth’s surface, map projection for portraying geographic features on the ground in the 2D surface, data model for representing spatial and non-spatial aspects of the reality (e.g., vector, raster, table), and visual variables (e.g., color hue, size, color value, shape) for cartographically representing attributes of geographic features on the Earth’s surface . 3. Algorithms (i.e., a step-by-step procedure to solve a problem) Examples: Examples of algorithm is breaking down the problem smaller steps and following them step-by-step as in creating the map from extracting the data to adding the data, manipulating the spatial data to visually represent information and create the final map with map elements . It also includes use of operations such as data classifications , selection query, table join, overlay, interpolation, buffer, etc. or use of programming concepts like conditional statement or loop to control workflow. 4. Programming Concepts (i.e. using data, functions, conditions, loops, formulas, etc.). Examples: Examples of programming concepts includes using different types of data ( spatial , attributes – quantitative and qualitative, and pixel-based raster data), working with databases, classifications , computations using formulas, functions, conditional statements, and loops for manipulating data, representing information or control workflow. MCD Learning Outcome-2: Analyze/Describe Algorithm** ______________________________________________________________________________ © Nandhini Gulasingam 3