Wanted: matching world maps of EEZs and marine productivity

I want to show to my students that only a minority of fish are caught in the high seas, and it would be nice to compare a world map of EEZs to a world map of marine and coastal productivity. So far I've found two sources of such information:

• GRID-Arendal's productivity map (eps or jpg)
• Marine Regions' EEZ map (shapefile)

For the time being I combined those but the result is not exactly pretty:

(Click on the picture for a closer look.)

The problem is that

1. The two maps have different centres (Pacific Ocean for GRID-Arendal, Atlantic Ocean for the EEZ map)
2. The two maps have different projection methods (Gall-Bertin for GRID-Arendal, and I haven't the faintest idea how to project the EEZ map as such in QGIS)

Are there any maps (preferably shapefiles with the same coordinate systems) of fishing yields and EEZs? Preferably with the Pacific Ocean in the centre? I know the latter is not standard but the Pacific is way more interesting than the Atlantic in this respect.

A simple Python script to make a literature table

Geeky post again - no math this time, but computer code.

I'm sure people have done this before, but I thought it would be a nice opportunity to practice my Python skills to write a small script for the following problem.. Usually when I read a scientific article I watch out for the following elements:

• Innovation: what does the study do what others haven't done before?
• Method: what method did they use?
• Data: where did they get their data from?
• Results: what are the main results?
• Relevance: who benefits from this research, and how?

I also like to place the research in one of the four quadrants in this post. I find it helpful to make an overview of these questions in a table:

1st author	Year	Journal	Quadrant	Innovation	Method	Data	Results	Relevance
Kompas	2005	Journal of Productivity Analysis	4	Estimates efficiency gains quota trade for Southeast Trawl Fishery, AU	Stochastic frontier analysis	AFMA and ABARE survey data	ITQs gave efficiency gains	Policy debate on ITQs
Kompas	2006	Pacific Economic Bulletin	3	Estimates optimal effort levels and allocation across species	Multifleet, multispecies, multiregion bioeconomic model	SPC data	Effort reduction needed; optimal stocks larger than BMSY	Policy debate on MEY

But here's the problem: I usually make my notes in a bibtex file (as a good geek should), which looks like this:

@ARTICLE{Kompas2006PacEconBull,
  author = {Kompas, T. and Che, T.N.},
  title = {Economic profit and optimal effort in the Western and Central Pacific tuna fisheries},
  journal = {Pacific Economic Bulletin},
  year = {2006},
  volume = {21},
  pages = {46-62},
  number = {3},
  data = {SPC data},
  innovation = {Estimates optimal effort levels and allocation across species},
  quadrant = {3},
  keywords = {tuna; bioeconomic model; optimisation; Pacific},
  method = {Multifleet, multispecies, multiregion bioeconomic model},
  results = {Effort reduction needed; optimal stocks larger than BMSY},
  relevance = {Policy debate on MEY}
}

@ARTICLE{Kompas2005JProdAnalysis,
  author = {Kompas, Tom and Che, Tuong Nhu},
  title = {Efficiency gains and cost reductions from individual transferable quotas: A stochastic cost frontier for the Australian South East fishery},
  journal = {Journal of Productivity Analysis},
  year = {2005},
  volume = {23},
  pages = {285-307},
  number = {3},
  quadrant = {3},
  data = {AFMA and ABARE survey data},
  innovation = {Estimates efficiency gains quota trade for Southeast Trawl Fishery, AU},
  keywords = {individual transferable quotas; stochastic cost frontier; fishery efficiency; ITQs},
  method = {Stochastic frontier analysis},
  relevance = {Policy debate on ITQs.},
  results = {ITQs gave efficiency gains}
}

I don't want to copy it all by hand, so I wrote this little script in Python to convert all entries in the bibtex file to a csv file:

import csv
from bibtexparser.bparser import BibTexParser
from dicttoxml import dicttoxml
from operator import itemgetter

def readFirstAuthor(inpList,num):
    author1 = ""
    x = inpList[num]['author']
    for j in x:
        if j != ',':
            author1+=j
        else:
            break
    return author1

def selectDict(inpList,name):
    outObj = []
    for i in range(len(inpList)):
        if name in inpList[i]['author'] and \

            inpList[i]['type']=='article':
            outObj.append(inpList[i])
    return(outObj)

def selectFieldsDict(inpList,fieldNames):
    outObj = []
    for i in range(len(inpList)):
        temp = {}
        for n in fieldNames:
            if n == 'author':
                author1 = readFirstAuthor(inpList,i)
                temp['author'] = author1
            else:
                if n in inpList[i]:
                    temp[n] = inpList[i][n]
                else:
                    temp[n] = 'blank'
        outObj.append(temp)
    return(outObj)

fieldnames = ['author','year','journal','quadrant',\

    'innovation','method','data','results','relevance']

with open('BibTexFile.bib', 'r') as bibfile:
    bp = BibTexParser(bibfile)
    
record_list = bp.get_entry_list()
record_dict = bp.get_entry_dict()

dictSelection = selectDict(record_list,'Kompas')

fieldSelection = selectFieldsDict(dictSelection,fieldnames)


test = sorted(fieldSelection, key=itemgetter('year'))


test_file = open('output.csv','wb')
csvwriter = csv.DictWriter(test_file, delimiter=',',\

    fieldnames=fieldnames)
csvwriter.writerow(dict((fn,fn) for fn in fieldnames))
for row in test:
     csvwriter.writerow(row)
test_file.close()

If you are a Python developer: any comments on this are welcome. I'm sure it's not perfect.