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Madagascar Projection Transformations

发布时间：2022-12-02

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Madagascar Projection Change Exercise

Madagascar Projection Transformations

Geospatial Fundamentals

Details of assessment:

You have been given a digitised version of a Mercator projection map and asked to produce a Lambert Conformal Conical map of Madagascar for an airplanes company so they can plan sight- seeing flights across Madagascar. You will carry out a set of conversions and/or transformations to convert from one co-ordinate systems to another using Python.

Intended Learning Outcomes:

By the end of this project you should be able to;

• Use some simple data and map projection equations to carry out a set of co-ordinate conversions.

• Develop your python skills to carry out a sequence of analysis steps and produce a desired numerical conclusion.

• Produce a short report discussing the relevant background, and methods employed as well as describing any short comings in the approach and considering other possible outcomes.

Deliverables:

Submit to Moodle the following THREE things:

1. A report in Word containing the following information (docx or .pdf):

a. A brief background on Mercator projection and Lambert Conformal Conic projection

b. A brief overview of the different stages of the projection change process, showing how they fit together.

c. Selected results section – anything you need to be able to discuss in the discussion section. Put your full numerical results for each stage in Appendix A of the report. See page two of this document for details of the stages.

d. Plots of the coastline in Mercator projection coordinates and LCC coordinates (e.g., in python or ArcGIS)

e. A discussion of the differences between the coastline in the Mercator projection coordinates and the LCC projection coordinates.

f. A discussion of any change in the results from using Least Squares Analysis and all four control points to estimate the transformation parameters, rather than using just 2 control points.

If you are happy that you have completed all of the above sections well and according to the instructions, and you chose to do optional extra original work, you may add an optional extra section explaining your optional extra ‘work’ . If you complete the standard sections well, you should get good marks without optional extra work.

2. A Word document with the text of all your python programs copied and pasted in.

3. A .zip file, containing all your python programs and data .txt files.

Stylistic Hints:

Some things we will be looking for when marking:

• Clear concise explanations (i.e., short and clear is better than longer and complicated sentences).

• Correct answers. Use Earth radius 6378000 m.

• Make sure numbers have units where appropriate and use a reasonable number of significant figures in results.

• A clearly laid out report that is easy for the reader to understand – suitable headings, no tiny text/fuzzy diagrams (refer to the how to write a technical report document).

• Nice, tidy, easy to understand Excel spreadsheets (i.e. enough text so someone who is not familiar with the exercise can easily understand what the spreadsheet is supposed to do).

• Well organised*, carefully commented python programs that are easy to understand. (*e.g. header explaining program purpose, library imports and most variables declared in one section at beginning of program).

• Your discussion section is where most of the range of marks will come from – make sure that your discussion is quantitative, not just qualitative.

This is a summative assessment, contributing to your final module mark. You must work independently on your report. You may crosscheck numbers from your programs with others and help people in your group debug problems but the programs and results you submit must be all your own work.

Stages of the calculation:

Stage	Calculation	Result
Python Program 1	Convert control point geographical coordinates to Mercator Projection grid coordinates.	Result A: Mercator Projection grid coordinates of control points 1-4.
Python Program 2	Estimate Digitizer to Mercator 2D similarity transformation constants using 2 control points (number 1 and number 3).	Result B: Transformation constants estimated using 2 control points.
Python Program 3	Coastline Digitiser Coordinates to Mercator coordinates using transformation constants (2 control points).	Result C: Coastline points in Mercator Projection grid coordinates.

Python Program 4	Convert Mercator Projection grid coordinates to geographic coordinates.	Result D: Coastline points in geographic coordinates (Lat, Long).
Python Program LSq.1	Estimate Digitizer to Mercator 2D similarity transformation constants using 4 control points and Least Squares].	Result F: Transformation constants estimated using 4 control points and Least Squares.
Python Program LSq.2	Coastline Digitiser Coordinates to Mercator coordinates using transformation constants (4 control points).	Result G: Coastline points in Mercator Projection grid coordinates.
Python Program LSq.3	Convert Mercator Projection grid coordinates to geographic coordinates (4 control points).	Result H: Coastline points in geographic coordinates (Lat, Long).
Python Program LSq.4	Convert geographic coordinates (Lat, Long) of coastline points to Lambert Conformal Conic grid coordinates using Forward LCC projection equations.	Result J: Coastline points in LCC Projection Grid coordinates.