Some years ago the Norwegian Directorate for Nature Management contributed to the Oil for development program under The Norwegian Agency for Development (NORAD). The program contributes to projects in Africa, Asia and South America. Ghana was, and still is, one of the partners to the Oil for Development Program.
As part of this mission it was agreed with the Ghanaian partners that Ragnvald Larsen and Howard Frederick (as an external consultant) should visit EPA and other relevant institutions to establish a firmer basis for further collaboration on environmental data management.
The attached report is from work done in Ghana in 2012. The work was mainly done by Howard Frederick, but contributions were also done by Roger Lewis Leh (EPA) and Ragnvald Larsen.
A secret spot – a potential password? Hardly anyone knows about this 5 meter high rock face in the forests outside Trondheim.
Remembering passwords takes focus and time. I want to authenticate myself without having to write in a meanlingless stream of characters. I am on a weekly basis nagged by different systems to change my paswords.
We already have several biometrically based authentications metods. Fingerprints, iris scans, selfies, hand geometry and what have you. More are probably more to come. The jury is out on several of the metods. I will leave this to the experts.
Being a geographer my favourite authentication metod will rely on spatially referenced secrets buried deep inside my mind. I want to propose a novel metod for authentication. I want to use a map to navigate to a place which has a special meaning for me. It would basically work like this:
I am stating my username (written)
The system then asks me to authenticate my user status by asking me for one or several geographical position.
The position could be the answer to questions like these:
Where did you find your wallet when you lost it in 2012?
Where was your father born?
What is your favourite place to pick blueberries?
Where is the secret rockface outside Trondheim?
Where did you spend the night the 17th of november 1995?
and so on…
My answer would be made not by entering a string of characters. I would answer the question by panning and zooming a map to the particular place. My answer would be to place a pin somewhere.
The method could be strengthening by asking for a combination of several places, or by varying the required precision in my answer. The answer (coordinates backoffice) would then be used to establish a string which again is the authentication variable (password).
Here are some combinations of the screen based password:
User traces a path
Real position combinations
Actual position represents the password
transport between several position srepresents the password
A track based on movements represents the password
The method will of course have it’s weaknesses. But it could work. And if someone already made this – then please send me a link!
An other variation of this method could be physical location or relocation in a given pattern. This would of course require a positional system which can not be spoofed, but where the position and its reporting is possible to confirm.
Seems like I am pulled to the coastlines. Seems like I am not alone. People tend to gravitate towards food resources, and where the ocean meets land, and even rivers, food has always been plenty. Trade too. And the boats of course.
I have a house, in the northern parts of Norway where the shoreline is but 200 meters away. From where I live in Trondheim I can see the fjord. The ocean has always been around.
I am one of those with fond memories of small waves, a grandfather safely steering the boat, and days blessed with sun and fishing. When the weather was less than fair we stayed in or near my grandparents house enjoying our small adventures around the old farm.
Seems also that the places I travel to these days are close to the ocean. Zanzibar is one of these fantastic places where oportunities and challenges arise practically on the shoreline. Mapping those oportunities and challenges is part of what I do. It invoves drones, big databases, partners in many institutions, researchers and more. Sometimes it comes together in a map describing the sensitivities of a coastal area. I like the thought of it, but fear that we more often than not do not have enough knowledge to present detailed enough maps.
In September we took some time off from our QGIS workshop to play around with the EPA drone. Great fun in the tennis court.
Ghana is an other of those places. My stays are usually in Accra, and although the coastline is
never far away it is merely the frame of the ocean and not much more. Inaccessible because of restricted daytime for me, the workshop participant and meetings participant. Workshops outside Accra usually end up being in Sogakope, a 60 minute drive down to Keta. But my meetings usually revolve on issues related to the ocean and its shoreline.
What’s with the coastal areas then? Here are some of the processes I am involved in:
Coastal sensitivity analysis for emergency response
Digitalization of drone data in coastal areas (coastline/mangrove/substrate)
Methods development of sensitivity assessments (both coastal and terrestrial)
I have attached a video from a recent (September 2017) trip to the Keta Lagoon area in Ghana. On a small strip of land between the Keta Lagoon and the coastline thousands of people live their lifes. The inland areas which are not flooded (remember this is a lagoon) are occasionaly flooded rendering the areas uninhabitable for parts of they year.
In the video you can see the sand traps designed to “harvest”sand so that the thin strip does not erode. You can see this as big dumps of stone perpendicular to the shoreline.
Apart from letting us see some of the areas near the coast the video also shows some highlights form a QGIS training near Sogakope.
Mangrove coastline near Ikwiriri south of Dar es Salaam (from OpenStreetMap)
I’m discussing coastlines with a friend. We agree, and we do not agree. In part because I do not really know myself what a proper coastline is. Purpose is the word. -What is the purpose of the coastline, I recall us discussing. When you choose to include purpose into a discussion things could start to get fuzzy.
Is the coastline mapped with a purpose something different than the factual coastline? Hm… Well the purpose this time is to have information which later can be used for a sensitivity analysis.
Openstreetmap definition of the coastline is as follows: “The natural=coastline tag is used to mark the Mean high water spring which is the point of the highest tide. See natural=coastline for all the details. See also de:Coastline for much more details.” So there is a clear definition, but if the purpose trumps the standard suggestion…?
Despite the clear definition Openstreetmap mapping remains a highly subjective thing. It keeps humans in the loop – for better or worse. I am one of those people. I having traced thousands of features with thousand and thousand more vertexes I know that tracing features involves many decisions. The problem is that the purpuse is not as neutral and objective as I would like it to be. Humans will for better or worse continue to confuse the picture.
Some years ago I spent what must have been 20 hours tracing Tanzania coastline and mangrove areas. I thought I did well. Now I am not so sure.
What is the right choice when mapping mangroves? Is there an outer or an inner coastline?
If you map the coastline with the purpose of establishing a basis for describing the sensitive parts of a coastline, placing the coastline inside the mangroves could be a problem. Placing it outside might be a better choice for this purpose. Some times, regardless of your purpose, there is not just one right way of doing it.
I believe we need guidelines for this work which are strict. We can allways process ourselves around the issue. For the purpose of a sensitivity atlas we can “snap” the coastline to the outermost mangrove if necessary. Right?
The problem with our opensource community is that we are missing the FME part of it. Safe software is facilitating for fast conversions between formats – and they also add processing into the equation. I am starting to think that not having a sweet user interface as we can find in FME makes us lazy. When given having to make decisions for our mapping we bring purpose to the table because the transformations are too manual.
Jeg har laget noen ortofoto av de nordligste delene av Mindværet. Mindværet er en skjærgård som ligger rett sør for øya Mindland på helgelandskysten. Øyområdet er godt egna for dagsturer med båt/kajakk. Det fins mange naturlige ankringssteder.
Området har et rikt fugleliv. Det er dessverre amerikansk mink i området, noe som går sterkt ut over fuglelivet i området.
Ortofotoene er satt sammen av 1.355 enkeltbilder tatt fra en høyde på 120 meter over havet. Bildene er bearbeidet ved hjelp av spesialprogramvare.
Et spesielt vakkert parti med svært gode muligheter for både fortøyning og bading.
Sammenstillingen er laget med utgangspunkt i bilder som er i berøring med landområder. Oppløsningen på kartene er i full størrelse så god at de kan egne seg til utskrift i størrelsesorden 1×1 meter.
Kartene kan lastes ned og kan benyttes vederlagsfritt.
Ortofotoet viser de største øyene i området. Sjøområdene mellom mer ikke tatt med. Filen er om lag 150 Megabyte stor og kan lastes ned. Et trykk på bildet vil ta deg til en nedlastingsside.
Ortofotoet/kartet viser de største øyene i området. OpenStreetMap er benyttet som bakgrunnskart. Dermed vises også øyer i omrdet uten ortofoto. Filen er om lag 125 megabyte stor og kan lastes ned. Et trykk på bildet vil ta deg til en nedlastingsside.
Brørholmen ligger et par hundre meter fra Mindtangen på Mindland. Kartet/ortofotoet er satt sammen av 95 separate vertikalbilder tatt med drone. Bildene er tatt fra en høyde på 120 moh. Bildene er tatt om ettermiddagen den 19. juli 2017.
Det er fritt frem å laste ned og å skrive ut kartet til utskrift og lignende.
Mulig det dukker opp flere slike bilder fra Mindland i løpet av sommeren.
Front page of the study of coastal and marine data sets in Tanzania
A report on coastal and marine environmental data in Tanzania has been published. The work was done by COWI Tanzania. This article gives a general overview of the inherent challenges in environmental data management as well as an overview of the report.
Environmental data management is a crucial part of any decision making related to the local, regional, national and global aspects of the environment. Good quality, up-to-date, accessible and relevant information is the foundation of good decision making in governments. Likewise, emergency response systems rely on sound information about sensitive areas and resources.
Since my first professional meeting with the management of spatial data in Tanzania back in 2004 I have struggled with issues related to collection, storage and dissemination of environmental information. As a GIS professional one soon discovers that the path towards a good spatial data infrastructure for environmental information is complicated by access to resources, thematic knowledge, economy, legal issues and the will to make data available. Tanzania is no exception – and it is not the only country with these challenges.
Earlier this year COWI Tanzania was contacted to undertake a “Study of coastal and marine datasets in Tanzania”. The background for the study was that many government offices, organizations and companies in Tanzania struggle with the different aspects of collecting, storing and disseminating their own information. I am happy to have been part of principal contact persons for this project on behalf of NEMC and NEA. The work done by COWI is solid and comprehensive.
Scope and definitions decided used in this report:
The geographical scope covers districts in Tanzania with a coastline, the coastline itself and coastal and marine areas.
Stakeholders are governmental agencies, research institutions, non-governmental organisations (NGOs) and others. A shortlist of institutions, based on the EIN workshop in October 2014 is provided, which does not limit the scope, but serves as a point of departure for the study.
Key informants are persons working in the institutions identified or related to the operations of such institutions.
Environmental data/information in this respect include spatial data files, reports containing tables and maps, Excel sheets, data collection methods description, etc. It is not restricted to ecological/biological data, but can also be other relevant baseline data (e.g. infrastructural, bathymetrical, meteorological and socio-economic data, etc.).
Spatial data can be understood as data that indicates geographic information related to features and boundaries on Earth, such as natural or constructed features, oceans, or similar.
Metadata is information that describes key characteristics of some original data, e.g. its content, conditions or locations.
Spatial Data Infrastructure is understood as a system for indexing data through metadata that enables users to describe or understand the scope, type or relevance of the original data.
The deliverables requested and delivered from the assignment includes:
A list of all visited and all contacted stakeholders;
A list of existing coastal and marine data, gaps in this data and data gathering projects;
Information about the datasets or documents, with a special focus on coastline and mangrove datasets, documented in an Excel sheet designed by the Consultant in collaboration with NEA. The information includes geographical extent, categories, custodians and owners;
Where possible, additional information about source for data and a brief note about
A description of metadata attributes according to the provided category system and, where possible, ISO/TC 211 and ISO 19115 2003;
Indication of the stakeholder willingness and ability to contribute to a collaboration under the Environmental Information Network with their data;
An overview of current developments/status related to a national spatial data infrastructure in Tanzania;
Suggestions for further work; and
A presentation at a marine data workshop,
The report has now been published and is available for download here in both pdf- and word-format:
At the Environmental Data Management workshop held by National Environment Management Council (NEMC) in October 2014, the stakeholders present concluded that the establishment of an Environmental Information Network (EIN) headed by NEMC and supported by the stakeholders represents a rational way forward. NEMC’s workshop in October 2014 and associated processes were funded by the Norwegian government through the Oil for Development Program (OfD) coordinated by Norwegian Agency for Development Cooperation (NORAD). The above report is part of bringing a national coordinated effort forward.
Open Street Map mangrove mapping in process north of Marendego, Tanzania
I have earlier written a blog post about Geoinformation Policy in Tanzania. Together with the authors of this report we will present this work as well as an evaluation of the environmental spatial data infrastructure in Tanzania at the TAWIRI conference early in December 2015.
Like many other Europeans I have had my share of visits to the Canary Islands (three and counting). The climate is decent from February until November. Some even like it in December and January. Most of us go to the Canary Islands for sunbathing, for long walks, to swim, spend time with friends or just get away from it all.
Few, if any, go to the Canary Islands for the wilderness. In this article I will be looking at your options if you were interested in getting away from people in the Canary Islands. It is not easy, but it is possible. If you are looking for wilderness in the Canary Islands I would suggest going to Lanzarote. Parque National de Timanfaya is the easiest accessible wilderness area.
The above map is based on publicly available vector data from the OpenStreetMap-project covering the Canary Islands. A wilderness analysis based on insufficient data will only represent a map of more or less mapped areas. In this case the basis for the analysis is decent. There will still be many errors.
The analysis has not in any way been sanctioned by Spanish authorities. It will not be used by the authorities and the audience (GIS geeks) of this posting is anyway quite limited.
The criterias used to map the wilderness areas based on OpenStreetMap in the Canary Islands are as follows:
Area with wilderness and encroachment. Visualization of change of wilderness status.
Wilderness degradation happens when new encroachments are made changing the wilderness status of an area. It is a complex issue which does not easily lend it self to a GIS based analysis. I will refer to my posting on wilderness for a peek into the complex world of wilderness philosophy.
It is possible to set up a system like FME to do an analysis of changes in wilderness due to new encroachments. The procedure I made generates a wilderness degradation data set based on wilderness and (new) encroachment data. It is based on procedures used by the Norwegian government in their analysis of wilderness and encroachment. The system should however be easy to accommodate for different preconditions by manipulating the number of wilderness zones and/or their buffer distances.
To visualize the degradation of wilderness it is necessary to make a categorization and furthermore establish a cartography to carry the information to the reader. In my view this can not be done unless the author/mapmaker to some extent takes side in what is good or not good related to wilderness degradation. This will be the focus on a forthcoming posting, but I will touch into the issues here as well.
FME is but one of the potential solutions for producing the results. QGIS, GeoKettle and even PostGIS could be good alternatives. The GitHub project has room for alternative implementations.
A philosophical approach towards wilderness analysis has been written. A FME-script has been designed and appropriately discussed. Now time has come to run the script on national level data to prove the concept and also open up for a discussion on challenges with the method and its results.
I have done this for several countries and will deliver the results and discuss them appropriately.
All examples are delivered as-is. There is no governmental context to the analysis. The data used for the analysis is OpenStreetMap data.
At the time of writing this article the number of examples has yet to be decided. The following are available as of writing this posting: