Tuesday 27 of June 2017 is the official release date of 2016 Census data. The first batch of information contains a comprehensive list of demographic and socio-economic topics for areas down to local neighbourhood level.
The ABS Census of Population and Housing is conducted every five years and provides a comprehensive picture of Australia's people, how they live and where they reside. The information collected during the Census is used to distribute government funds and plan services for the community – including those relating to housing, transport, education, industry, hospitals and the environment.
Census data is also used by individuals and organisations in the public and private sectors to make informed decisions on policy and planning issues that impact the lives of all Australians. And of course, Census data is also a valuable source of statistics for research and data analysis in support of business activities as it provides comprehensive information on potential customers and markets.
MapDeck will be adding the 2016 Census data to its inventory progressively from the day of its official release, starting with free excerpts of statistics for Postal Areas.
All relevant Census 2016 related statistical geography is already available on MapDeck for immediate perusal. This includes, for example, Mesh Blocks, the smallest statistical geography units and building blocks for all other larger structures, like suburbs or postal and local government areas, etc.
A unique feature of Mesh Blocks is that each one is classified according to a dominant use of the area, like residential, parkland, education, commercial, etc. This makes it possible to use mesh blocks to create simple maps of land use, as the one below for the ACT.
Imagine the possibilities… Sign up for free MapDeck account today (your invite code is: c3df6244 )!
Map: ACT Mesh Blocks by Category, 2017
Showing posts with label Feature Articles - All Things Spatial. Show all posts
Showing posts with label Feature Articles - All Things Spatial. Show all posts
Tuesday, June 27, 2017
Monday, November 28, 2016
Do the rich always get richer?
Let’s tackle this controversial topic as an example of how thematic maps and simple in-map analysis can bring clarity when dealing with complex issues or situations.
'The rich get richer and the poor get poorer' is a common catchphrase that is often evoked in discussions about economic inequality. However, is this a true reflection of reality or just a widely held belief that is taken at face value? How do we tell?
It would probably take a few months to fully investigate all the factors that play a role in the complex issue of wealth concentration and to formulate a solid argument which could withstand full academic scrutiny.
However, as often happens in real life, we rarely have the necessary resources at our disposal, or the time, to do extensive research to get to the bottom of the issues we encounter. Therefore, a credible, quickly-determinable clue as to what that answer may be, and which allows us to make a more informed decision, is very often the most practical approach.
Map Deck's newly released Thematic Mapper app - with a unique in-map analytics functionality - offers everyone, even the most novice users, a simple solution that can aid in the search for answers to complex questions. Let’s explore how…
To begin our analysis we need information on the wealth of individuals, as well as statistics on how that wealth is changing over time.
Data on taxable incomes for postcodes for 2003–04 and 2013–14 income years, published by the Australian Taxation Office (ATO) and available for free perusal with MapDeck apps, is a good proxy. This information will give us an opportunity to test whether this widely held belief about rich getting always richer holds true in a spatial context. And in the process, we will also demonstrate how simple mapping tools can provide a reasonable answer - and quickly.
The objective here is to test if top taxable incomes grow consistently at or above the growth rate of taxable incomes of the entire population of Australian taxpayers.
If yes, then this would indicate that wealth of the richest individuals is increasing faster than wealth of the rest of the population and consequently, that poorer people are becoming relatively poorer over time. This is a simple argument but proving or disproving it will be enough to give us a strong clue as to the likely conclusions that would be obtained from more thorough research.
It is always a good idea to clearly articulate assumptions and to identify limitations of the data you work with as it is not always possible to have the best and the most accurate information on hand for analysis.
In this case, for example, we defined “the rich” as taxpayers residing in postcodes with median taxable incomes in the top 10% in 2003-4 and in 2013-14.
The limitation of this approach is that we are looking only at the incomes and not the overall wealth of individuals. However, taxable incomes are a reasonable proxy for wealth. That is, wealth usually generates an income stream and even if only a part of that income is attributable to an individual, for example, due to the use of various tax minimisation schemes, growing wealth will be reflected in the growth of taxable incomes of those individuals.
We also assume here that residents of any given postcode are all alike which is an oversimplification but not an unreasonable one. In particular, it is true that postcodes comprise of populations with varying demographic and socio-economic status but summarising their collective characteristics into a single measure is an acceptable practice in data analysis. Therefore, a change in median taxable income for a postcode is a good representation of the change in wealth of its residents.
More of an issue is the fact that we are assuming that the 2016 postcode boundaries are exactly the same as those in 2013-14 and in 2003-04. However, yet again, this is a reasonable assumption since postcodes with the top taxable incomes are predominantly in capital cities and capital city postcodes remain relatively stable over time.
More assumptions and caveats could be listed but the main point here is the importance of being aware of the limitation of the data you are working with. It applies to any data and information, not only to that with spatial context.
It takes just a few moments to set up a map ready for analysis using MapDeck’s Thematic Mapper app. Firstly, we select and import into Thematic Mapper the postcode boundaries and ATO data, then join them using the data mixer functionality.
We are interested in only 3 columns from the newly created table: the median taxable incomes for both 2003-4 and 2013-14, and the percent growth of median taxable incomes over the 10 year period. The information on the number of individual taxpayers per postcode is included just for context.
Selecting the “complex legend” option enables advanced data filtering functionality in Thematic Mapper. We set taxable income values to show the top 10% range only (these values are calculated and can be looked up when setting up legend styles for individual data sets from our table).
The last step is to remove empty postcodes from the map (that is, those with no data) and to import a map overlay showing main roads and localities to give our map additional spatial context (the Stamen’s Hybrid layer in this case).
Let’s now review the map, summarise the data and draw some conclusions.
From the map legend we know that:
The pattern we were looking for (that is, where all top postcodes are coloured in shades of green, indicating growth rates over 50%) is present on both the Perth and Brisbane maps but not on the Melbourne or Sydney maps. Therefore, we can conclude with confidence that it is not entirely true that the rich are getting richer and the poor are getting poorer all the time and in every place.
In other words, our data and simple in-map analysis indicate that some rich neighbourhoods grew their incomes at less than the national median rate, hence getting relatively ''poorer'' over time with respect to the rest of the population.
Of course, this is only a clue as to what conclusions may possibly be drawn from more thorough research. However, the point is that is takes very little effort to conduct this kind of analysis using MapDeck’s online app and huge range of available data. This approach is not suited for all situations but it can provide valuable insight, and fast, to adequately support many important every day decisions.
Median Taxable Incomes, Australia 2003-4 and 2013-14 map and ATO data table used in this case study are available for free perusal for registered MapDeck users.
And for a limited time, all MapDeck users have the opportunity to try Thematic Mapper at no cost. The app is available for a free one month subscription until 20 December.
So, what is the big issue you would like to explore next? Use this invite code to sign up if you are not yet MapDeck user: f10dc1f2
About MapDeck:
MapDeck is an online marketplace for spatial information and simple-to-use, task-oriented tools to support a variety of activities, be it business or investment related, environment, community or policy focused.
We make spatial information and analysis ready data accessible to all. Locate, map, act!
'The rich get richer and the poor get poorer' is a common catchphrase that is often evoked in discussions about economic inequality. However, is this a true reflection of reality or just a widely held belief that is taken at face value? How do we tell?
It would probably take a few months to fully investigate all the factors that play a role in the complex issue of wealth concentration and to formulate a solid argument which could withstand full academic scrutiny.
However, as often happens in real life, we rarely have the necessary resources at our disposal, or the time, to do extensive research to get to the bottom of the issues we encounter. Therefore, a credible, quickly-determinable clue as to what that answer may be, and which allows us to make a more informed decision, is very often the most practical approach.
Map Deck's newly released Thematic Mapper app - with a unique in-map analytics functionality - offers everyone, even the most novice users, a simple solution that can aid in the search for answers to complex questions. Let’s explore how…
To begin our analysis we need information on the wealth of individuals, as well as statistics on how that wealth is changing over time.
Data on taxable incomes for postcodes for 2003–04 and 2013–14 income years, published by the Australian Taxation Office (ATO) and available for free perusal with MapDeck apps, is a good proxy. This information will give us an opportunity to test whether this widely held belief about rich getting always richer holds true in a spatial context. And in the process, we will also demonstrate how simple mapping tools can provide a reasonable answer - and quickly.
![]() |
| Distribution of taxable incomes (deciles) by postal areas - Adelaide [click to enlarge] |
The objective here is to test if top taxable incomes grow consistently at or above the growth rate of taxable incomes of the entire population of Australian taxpayers.
If yes, then this would indicate that wealth of the richest individuals is increasing faster than wealth of the rest of the population and consequently, that poorer people are becoming relatively poorer over time. This is a simple argument but proving or disproving it will be enough to give us a strong clue as to the likely conclusions that would be obtained from more thorough research.
![]() |
| Change in taxable incomes over a decade to 2013-14 - Adelaide [click to enlarge] |
It is always a good idea to clearly articulate assumptions and to identify limitations of the data you work with as it is not always possible to have the best and the most accurate information on hand for analysis.
In this case, for example, we defined “the rich” as taxpayers residing in postcodes with median taxable incomes in the top 10% in 2003-4 and in 2013-14.
The limitation of this approach is that we are looking only at the incomes and not the overall wealth of individuals. However, taxable incomes are a reasonable proxy for wealth. That is, wealth usually generates an income stream and even if only a part of that income is attributable to an individual, for example, due to the use of various tax minimisation schemes, growing wealth will be reflected in the growth of taxable incomes of those individuals.
We also assume here that residents of any given postcode are all alike which is an oversimplification but not an unreasonable one. In particular, it is true that postcodes comprise of populations with varying demographic and socio-economic status but summarising their collective characteristics into a single measure is an acceptable practice in data analysis. Therefore, a change in median taxable income for a postcode is a good representation of the change in wealth of its residents.
More of an issue is the fact that we are assuming that the 2016 postcode boundaries are exactly the same as those in 2013-14 and in 2003-04. However, yet again, this is a reasonable assumption since postcodes with the top taxable incomes are predominantly in capital cities and capital city postcodes remain relatively stable over time.
More assumptions and caveats could be listed but the main point here is the importance of being aware of the limitation of the data you are working with. It applies to any data and information, not only to that with spatial context.
It takes just a few moments to set up a map ready for analysis using MapDeck’s Thematic Mapper app. Firstly, we select and import into Thematic Mapper the postcode boundaries and ATO data, then join them using the data mixer functionality.
| Thematic Mapper: data mixer |
We are interested in only 3 columns from the newly created table: the median taxable incomes for both 2003-4 and 2013-14, and the percent growth of median taxable incomes over the 10 year period. The information on the number of individual taxpayers per postcode is included just for context.
Selecting the “complex legend” option enables advanced data filtering functionality in Thematic Mapper. We set taxable income values to show the top 10% range only (these values are calculated and can be looked up when setting up legend styles for individual data sets from our table).
The last step is to remove empty postcodes from the map (that is, those with no data) and to import a map overlay showing main roads and localities to give our map additional spatial context (the Stamen’s Hybrid layer in this case).
Let’s now review the map, summarise the data and draw some conclusions.
From the map legend we know that:
- Top deciles of median taxable income were $33,503 to $68,017 in 2003-4 and $51,128 to $113,687 in 2013-14;
- Median taxable incomes for individual postcodes grew between 7% and 257% over a decade to 2013-14;
- Median growth in median taxable incomes was 50% (that is, in half of postcodes, median taxable incomes grew by less than 50% in the 10 year period and in the other half the growth was equal to, or greater than, 50% - therefore the 50% growth rate can be used as a national benchmark for further comparisons).
| Growth in median taxable incomes 2003-04 to 2013-14, top income decile - Perth [click to enlarge] |
|
| Growth in median taxable incomes 2003-04 to 2013-14, top income decile - Sydney [click to enlarge] |
| Growth in median taxable incomes 2003-04 to 2013-14, top income decile - Melbourne [click to enlarge] |
In other words, our data and simple in-map analysis indicate that some rich neighbourhoods grew their incomes at less than the national median rate, hence getting relatively ''poorer'' over time with respect to the rest of the population.
Of course, this is only a clue as to what conclusions may possibly be drawn from more thorough research. However, the point is that is takes very little effort to conduct this kind of analysis using MapDeck’s online app and huge range of available data. This approach is not suited for all situations but it can provide valuable insight, and fast, to adequately support many important every day decisions.
Median Taxable Incomes, Australia 2003-4 and 2013-14 map and ATO data table used in this case study are available for free perusal for registered MapDeck users.
And for a limited time, all MapDeck users have the opportunity to try Thematic Mapper at no cost. The app is available for a free one month subscription until 20 December.
So, what is the big issue you would like to explore next? Use this invite code to sign up if you are not yet MapDeck user: f10dc1f2
About MapDeck:
MapDeck is an online marketplace for spatial information and simple-to-use, task-oriented tools to support a variety of activities, be it business or investment related, environment, community or policy focused.
We make spatial information and analysis ready data accessible to all. Locate, map, act!
Wednesday, May 14, 2014
Mapping migrants in Australia
Where do migrants live? Australian Bureau of Statistics has just published an interesting article that tries to answer that same question. We can learn from the article that:
A migrant is a person who has reported their country of birth as a nation other than Australia. Migration is an important contributor to Australia’s growing population. Since 2006, net overseas migration to Australia has contributed more to Australian population growth each year than growth through natural increase.
In the 2011 Census, there were 5.3 million migrants in Australia, which means one in every four (26%) Australian residents was born overseas. Australia’s migrant population is relatively large when compared with other Western nations. Taken as a proportion of the population, Australia has a larger migrant population than does New Zealand (23%), Canada (21%), the United States of America (13%) and the United Kingdom (13%).
The largest contributor to Australia's migrant population continues to be people born in the United Kingdom (UK). In the 2011 Census, 1.1 million UK-born migrants lived in Australia - around one in every twenty Australian residents.
In comparison to people born in Australia, migrants show a tendency to settle in major urban areas of Australia. While 64% of Australian-born people lived in a major urban area of Australia in 2011, 85% of those born overseas lived in a major urban area.
Within urban areas, migrants in Australia tended to live in Australia's two largest cities, a trend seen in Australia since the late 1940s. In the 2011 Census, just under half of all migrants in Australia lived in either Sydney or Melbourne, with 1.4 million residents of Sydney and 1.2 million residents of Melbourne being born overseas. Perth had the third largest migrant population in Australia at 568,000 people.
For further information on how to access detailed statistics on migrants in Australia please send your enquiries to info@aus-emaps.com.
Related Posts:
Census 2011 Online Maps
Census 2011 Online Maps User Guide
Mapping Australian social diversity
Mapping social diversity in NSW
A migrant is a person who has reported their country of birth as a nation other than Australia. Migration is an important contributor to Australia’s growing population. Since 2006, net overseas migration to Australia has contributed more to Australian population growth each year than growth through natural increase.
In the 2011 Census, there were 5.3 million migrants in Australia, which means one in every four (26%) Australian residents was born overseas. Australia’s migrant population is relatively large when compared with other Western nations. Taken as a proportion of the population, Australia has a larger migrant population than does New Zealand (23%), Canada (21%), the United States of America (13%) and the United Kingdom (13%).
The largest contributor to Australia's migrant population continues to be people born in the United Kingdom (UK). In the 2011 Census, 1.1 million UK-born migrants lived in Australia - around one in every twenty Australian residents.
In comparison to people born in Australia, migrants show a tendency to settle in major urban areas of Australia. While 64% of Australian-born people lived in a major urban area of Australia in 2011, 85% of those born overseas lived in a major urban area.
Within urban areas, migrants in Australia tended to live in Australia's two largest cities, a trend seen in Australia since the late 1940s. In the 2011 Census, just under half of all migrants in Australia lived in either Sydney or Melbourne, with 1.4 million residents of Sydney and 1.2 million residents of Melbourne being born overseas. Perth had the third largest migrant population in Australia at 568,000 people.
For further information on how to access detailed statistics on migrants in Australia please send your enquiries to info@aus-emaps.com.
Related Posts:
Census 2011 Online Maps
Census 2011 Online Maps User Guide
Mapping Australian social diversity
Mapping social diversity in NSW
Sunday, March 30, 2014
Cloud free base map image from MapBox
Cloud free, near real time, seamlessly integrated scenes of imagery acquired by various satellites orbiting the Earth is a holy grail of remote sensing community. However, achieving this result is not a trivial task due to volume of data and computational capacity required to process it. So, for now, we only have one-off attempts to create such mosaic with lower resolution imagery.
You may be well familiar with Blue Marble seasonal mosaics of MODIS imagery (~500m resolution) that appeared on many versions of online maps but the latest version created by MapBox is a great improvement on the previous, dated by now version, which deserve a mention.
The goal was to make the most beautiful image of an idealized, cloudless planet, “trapped in eternal summer”. As described by the creator, “It’s a completely natural product. Every pixel is a real pixel captured by a camera in the sky. But it’s also completely synthetic.”
You can preview the entire globe of cloud free imagery on MapBox’s tour page and the whole process of generating that image was well explained in Wired article in 2013.
Now the race is on to produce a higher resolution version (possibly based on Landsat 7 and Landsat 8 data) as well as to utilise all the possible band combinations to extract information, in near-real time, on glaciers, wildfires, crops, droughts and floods, cities and forests, surface temperature, plankton blooms, seasonal dynamics, smog, and myriad of other earth surface, near surface and shallow water objects. And most importantly – observe changes over time.
It is interesting to note that, yet again, innovation is driven by a relative newcomer to the spatial industry. As I pointed out in my 2013 end of year reflection, this is a very common pattern in the last couple of decades, where “old hands” just can’t recognise all the opportunities that technological advancement and computational capacity bring to this industry, and it takes outsiders to “spot the obvious”.
MapBox is definitely one of the more innovative companies that emerged in recent years and is worth watching - we can all learn from their fresh approach to old problems…
Related Posts:
New approach to satellite imagery analysis
Landsat 8 data explained
Google unlocks Landsat archives
Free high resolution imagery
You may be well familiar with Blue Marble seasonal mosaics of MODIS imagery (~500m resolution) that appeared on many versions of online maps but the latest version created by MapBox is a great improvement on the previous, dated by now version, which deserve a mention.
The goal was to make the most beautiful image of an idealized, cloudless planet, “trapped in eternal summer”. As described by the creator, “It’s a completely natural product. Every pixel is a real pixel captured by a camera in the sky. But it’s also completely synthetic.”
You can preview the entire globe of cloud free imagery on MapBox’s tour page and the whole process of generating that image was well explained in Wired article in 2013.
Now the race is on to produce a higher resolution version (possibly based on Landsat 7 and Landsat 8 data) as well as to utilise all the possible band combinations to extract information, in near-real time, on glaciers, wildfires, crops, droughts and floods, cities and forests, surface temperature, plankton blooms, seasonal dynamics, smog, and myriad of other earth surface, near surface and shallow water objects. And most importantly – observe changes over time.
It is interesting to note that, yet again, innovation is driven by a relative newcomer to the spatial industry. As I pointed out in my 2013 end of year reflection, this is a very common pattern in the last couple of decades, where “old hands” just can’t recognise all the opportunities that technological advancement and computational capacity bring to this industry, and it takes outsiders to “spot the obvious”.
MapBox is definitely one of the more innovative companies that emerged in recent years and is worth watching - we can all learn from their fresh approach to old problems…
Related Posts:
New approach to satellite imagery analysis
Landsat 8 data explained
Google unlocks Landsat archives
Free high resolution imagery
Saturday, December 14, 2013
Year's end reflection: State of spatial affairs
I have been following fortunes of spatial industry in Australia for more than a decade and, comparing to recent past, it seems that the industry is in a bit of a low right now. Not necessary in terms of revenues, or the level of activity, or overall relevance of the industry to the rest of the economy, but rather in terms of the level of excitement and enthusiasm amongst the participants. It is hard to tell why but things just seem to be lacking a bit of a spark.
The last time the entire spatial industry was abuzz with excitement was in 2005/06 when Google released its online map service. Back then, there was no conversation amongst spatial professionals and no presentation at conferences and seminars without a reference to Google Maps and enormous opportunity their release presented for the industry. And indeed, the follow up years were very good as the industry expanded in size (both in terms of the number of participants and attributed revenues) and in relevance, as it reached into a wide range of new fields and applications.
Although that level of excitement is rather unlikely to return any time soon it doesn’t mean that nothing interesting is happening in the industry right now. In fact, there are many developments to be excited about and although they may be happening on the fringes, it will not take long for them to filter through to the mainstream.
On the top of my list of the most significant developments is a giant leap in capabilities of web browsers - thanks in big part to consistent implementation of HTML5 in the latest versions. It makes it viable now to serve voluminous spatial data directly to the browser and render it in 2D as well as in 3D in real time. Animation and rich interactive visualisation of spatial data is now also possible, extending spatial industry well into the domain of interactive graphics and visual art.
SVG is back in favour and tiled vector data for base map presentation is the next obvious step in the evolution of online mapping applications. Better browser capabilities are also enabling replication of desktop functionality in an online environment – opening up myriad of opportunities for Software as a Service (SaaS) applications with advanced spatial functionality.
In the quest for greater efficiencies XML based spatial data exchange formats, that traditionally supported spatial web services, have been substituted with more processing friendly formats, like for example GeoJSON. The latest flavour is TopoJSON that preserves topology of spatial data and allows for more compact description of geometries since common points are recorded only once.
Spatial data storage and transfer formats is the area where a lot of innovation will be happening in the coming years. This is necessary to fully utilise “big data” and High Performance Computing (HPC) processing capabilities for spatial data. Traditional file formats and relational databases with spatial extensions are not compatible with these new paradigms in computing.
The volume of spatial data keeps growing exponentially as data capture and processing capabilities increase. We now have spatial data generated from a wide range of satellite, airplane and drone deployed sensors, but also from stationary as well as mobile devices on the ground. Information is captured in a variety of formats, such as optical, radar, LiDAR or multitude of measured observations but also can be derived from secondary sources. All that data can be streamed and processed in real or near real time into a variety of useful spatial information. The opportunities to “value add” to that data are overwhelming.
The access to that vast information base is getting easier as well since more and more State and Federal Government agencies release their spatial data at no cost to end users and with no restrictions on its use. Roads centrelines, cadastre and address information, administrative boundaries etc. data can now be combined with crowd sourced information to improve the accuracy and reliability of data already in the public domain. The flip side of this development is that traditional “data reseller” model may no longer be a viable business option.
Positional accuracy of spatial data will keep increasing as higher resolution sensors come online and as alternatives to GPS are launched - for example, European Galileo program. It is also viable now to establish ground based private high accuracy positioning infrastructure, capable of working indoors.
However, that variety of spatial data and speed of its creation also creates big challenges. Mainly in terms of efficient versioning, cataloguing and archiving of data and making it easily discoverable. This puzzle is yet to be solved so, whoever comes first with an acceptable solution is destined to reap a big reward.
I have barely scratched the surface with the above list since the boundaries of spatial industry are so expansive - and hence the number of possible factors influencing the industry is so large. The important point to note is that spatial industry never operated in a vacuum – it has always served a “bigger cause”. That is, the outputs created by spatial industry gave significant advantage to those who used them in pursuit of much grander objectives. In historical times, these outputs were maps for military operations and maritime endeavours. Today, it is a vast array of specialised tools, advanced theories and spatial information in myriad of formats that support almost every aspect of human activity.
As range of fields where spatial theory, data and tools can be applied expands, so does the industry. That “value adding” characteristic of spatial industry is the biggest opportunity for all the participants. However, it is also the biggest threat to industry’s identity, as various aspects of spatial technologies and practises get absorbed into much larger and/or more prominent industries and undertakings. As general level of education of the community increases, basic spatial skills and tools are becoming a commodity, like touch typing skills or word processing and spreadsheet software. Perhaps this is the biggest and the most far reaching development of all…
Whether you are in hardware, software and/or service side of the business, it is important to recognise not only what is happening in your part of the market but also in related fields. Take advantage of this quieter time to re-examine where your next big opportunity is likely to come from. As it happened with Google Maps, “the next big thing in spatial” may originate outside of the industry so, it is important to have a broad perspective to recognise the opportunities in advance and to get onboard early.
The last time the entire spatial industry was abuzz with excitement was in 2005/06 when Google released its online map service. Back then, there was no conversation amongst spatial professionals and no presentation at conferences and seminars without a reference to Google Maps and enormous opportunity their release presented for the industry. And indeed, the follow up years were very good as the industry expanded in size (both in terms of the number of participants and attributed revenues) and in relevance, as it reached into a wide range of new fields and applications.
Although that level of excitement is rather unlikely to return any time soon it doesn’t mean that nothing interesting is happening in the industry right now. In fact, there are many developments to be excited about and although they may be happening on the fringes, it will not take long for them to filter through to the mainstream.
On the top of my list of the most significant developments is a giant leap in capabilities of web browsers - thanks in big part to consistent implementation of HTML5 in the latest versions. It makes it viable now to serve voluminous spatial data directly to the browser and render it in 2D as well as in 3D in real time. Animation and rich interactive visualisation of spatial data is now also possible, extending spatial industry well into the domain of interactive graphics and visual art.
SVG is back in favour and tiled vector data for base map presentation is the next obvious step in the evolution of online mapping applications. Better browser capabilities are also enabling replication of desktop functionality in an online environment – opening up myriad of opportunities for Software as a Service (SaaS) applications with advanced spatial functionality.
In the quest for greater efficiencies XML based spatial data exchange formats, that traditionally supported spatial web services, have been substituted with more processing friendly formats, like for example GeoJSON. The latest flavour is TopoJSON that preserves topology of spatial data and allows for more compact description of geometries since common points are recorded only once.
Spatial data storage and transfer formats is the area where a lot of innovation will be happening in the coming years. This is necessary to fully utilise “big data” and High Performance Computing (HPC) processing capabilities for spatial data. Traditional file formats and relational databases with spatial extensions are not compatible with these new paradigms in computing.
The volume of spatial data keeps growing exponentially as data capture and processing capabilities increase. We now have spatial data generated from a wide range of satellite, airplane and drone deployed sensors, but also from stationary as well as mobile devices on the ground. Information is captured in a variety of formats, such as optical, radar, LiDAR or multitude of measured observations but also can be derived from secondary sources. All that data can be streamed and processed in real or near real time into a variety of useful spatial information. The opportunities to “value add” to that data are overwhelming.
The access to that vast information base is getting easier as well since more and more State and Federal Government agencies release their spatial data at no cost to end users and with no restrictions on its use. Roads centrelines, cadastre and address information, administrative boundaries etc. data can now be combined with crowd sourced information to improve the accuracy and reliability of data already in the public domain. The flip side of this development is that traditional “data reseller” model may no longer be a viable business option.
Positional accuracy of spatial data will keep increasing as higher resolution sensors come online and as alternatives to GPS are launched - for example, European Galileo program. It is also viable now to establish ground based private high accuracy positioning infrastructure, capable of working indoors.
However, that variety of spatial data and speed of its creation also creates big challenges. Mainly in terms of efficient versioning, cataloguing and archiving of data and making it easily discoverable. This puzzle is yet to be solved so, whoever comes first with an acceptable solution is destined to reap a big reward.
I have barely scratched the surface with the above list since the boundaries of spatial industry are so expansive - and hence the number of possible factors influencing the industry is so large. The important point to note is that spatial industry never operated in a vacuum – it has always served a “bigger cause”. That is, the outputs created by spatial industry gave significant advantage to those who used them in pursuit of much grander objectives. In historical times, these outputs were maps for military operations and maritime endeavours. Today, it is a vast array of specialised tools, advanced theories and spatial information in myriad of formats that support almost every aspect of human activity.
As range of fields where spatial theory, data and tools can be applied expands, so does the industry. That “value adding” characteristic of spatial industry is the biggest opportunity for all the participants. However, it is also the biggest threat to industry’s identity, as various aspects of spatial technologies and practises get absorbed into much larger and/or more prominent industries and undertakings. As general level of education of the community increases, basic spatial skills and tools are becoming a commodity, like touch typing skills or word processing and spreadsheet software. Perhaps this is the biggest and the most far reaching development of all…
Whether you are in hardware, software and/or service side of the business, it is important to recognise not only what is happening in your part of the market but also in related fields. Take advantage of this quieter time to re-examine where your next big opportunity is likely to come from. As it happened with Google Maps, “the next big thing in spatial” may originate outside of the industry so, it is important to have a broad perspective to recognise the opportunities in advance and to get onboard early.
Monday, November 25, 2013
New approach to satellite imagery analysis
Geoscience Australia has just released a short 3 min. video presenting a concept of “data cube” for storing and analysing Earth observation imagery acquired by satellites. This proof of concept application has been built to work with Landsat data and was already used in an operational capacity on several data analysis projects. This is the future of analysis of big volumes of temporal, remotely sensed data.
The concept can be extended to work with any data that can be referenced to a grid structure (in fact, to any spatial data that comprise of a collection of points in space). This is not the first attempt to work with cubed spatial data but certainly the first that I know of that is capable of processing terabytes of spatial imagery into a variety of derived information for immediate, practical use.
Related Posts:
Point cloud 3D map technology
Photosynth - big promise or just a fancy photo viewer?
The concept can be extended to work with any data that can be referenced to a grid structure (in fact, to any spatial data that comprise of a collection of points in space). This is not the first attempt to work with cubed spatial data but certainly the first that I know of that is capable of processing terabytes of spatial imagery into a variety of derived information for immediate, practical use.
Related Posts:
Point cloud 3D map technology
Photosynth - big promise or just a fancy photo viewer?
Thursday, October 10, 2013
Metadata – a problem that doesn’t go away
Last month I came across yet another case study that exposed spatial metadata standard as the primary cause of problems with a delivered data archiving, cataloguing and dissemination system. It is another statistic on a long list of failures caused by reliance on a flowed concept. I am an avid critic of the current metadata standard for spatial data and have written extensively about the reasons in my earlier posts so, I am not going to repeat previous arguments here again - you can find links to those posts at the end of this article. Criticism, however constructive, only helps to expose the problems, not to solve them. Therefore today I would like to share with you my thoughts on a better, more pragmatic approach to creating metadata for spatial information.
# The history
Briefly about the anatomy of the problem (maybe a bit overdramatised but with a good intention to highlight where things went wrong). I remember vividly the presentation on spatial data interoperability I attended about a decade ago. One of the key messages was that metadata is “a glue that will allow it all to happen”. No problem with that but when I asked a few questions regarding practical implementation, I was quickly hushed. One of the presenters did approach me after the event and admitted that they were aware of the potential problems but that they did not want to alienate the audience by bringing those issues to the forefront at that point in time. As he put it, there were enough benefits in the proposed approach that warranted overlooking potential issues in order to get the maximum buy-in from key stakeholders. This spin approach succeeded. In years that followed you only got to hear about “good things” and quite a number of people made a career out of selling the “metadata success story” from one continent to the other. And the myth “just follow the standard and everything will be ok” perpetuated… never mind the truth.
# Background on metadata and standards
Let me set the record straight - I do not dismiss the need for metadata. On the contrary – metadata is a very, very important aspect of any data creation, maintenance and dissemination activity. But I question the usefulness of current metadata standards to support those tasks adequately.
“Standard” is just a set of conventions that a community of practice agrees to follow. It could be designed by a committee (pun intended) or arrived at by wide acceptance of a common practice (ie. when “someone’s way” of doing things is liked and followed by others). But standards should never be treated as a formula for success … Unfortunately, this is how ISO 19115 Geographic Information - Metadata standard has been sold to GIS community…
At a theoretical level, the concept of metadata is very simple. It is just “data about the data”… But this is where the simplicity ends and complexity begins because you quickly discover that metadata is useful not only for spatial data but also for spatial information in any format (ie. printed as well as electronic, single point to compilations of 100’s of layers, vectors as well as rasters and grids… and point clouds… one-off or dynamically generated on the fly… and whatever else you want to class as “spatial”).
# Why metadata implementation projects fail
A typical metadata implementation project goes like this - an organisation accumulates more and more data, to the extent that it causes problems for the IT department to manage it. So, a project is initiated to “catalogue what we have”. And in order to catalogue systematically what you have you need to describe it in a consistent way - you need metadata. The obvious step is to search if there are any standards that will help to deal with the issue rather than reinventing the wheel.
It does not take much effort to find the spatial data metadata standard documentation. So, you start reading and quickly realise that you cannot make any sense of the gobbledygook of the official documentation. A thought inevitably crosses your mind - “I need an expert!”. And of course you look for… “the expert in ISO 19115 metadata standard” . In the end, you get what you ask for - an expert in the standard, not the expert in solving your kind of problems. That expert cannot advise you otherwise but just to “follow the standard and you will be right”.
The expert usually brings a set of recommend tools (which of course are built around ISO metadata standard) and you also get help in implementing your catalogue/system “by the book”. All good, great success… Until you realise that a classic “garbage in / garbage out” principle applies here as well… what a surprise!
You see, the failure is built into the solution (it is the metadata standard!) so, it is very rare that this approach delivers. You don’t believe me? Just talk to those who have to use the information contained in metadata records (not those who implement metadata standards and build systems!)…
Ok, to be fair, there is one exception where you can achieve an acceptable outcome (but I caveat this statement depending on how you define “acceptable”). It will happen only if you rule with an iron fist what information goes into your metadata. However, it can get out of hands easily if you have a lot of data to deal with (so you take shortcuts to process it all in bulk), a lot of different people writing metadata content (so, there are different views of what is important and needs to be recorded – because the standard allows this), and/or a lot of different types of data or data that grows rapidly (lack of consistency of information or sheer volume of data makes it impossible to record all the useful details). And let me stress this again, there is no guarantee that content of your metadata will be of any use outside of your organisation or immediate community of interest because it may lack the information perceived by others as vital (see my earlier post for more explanations).
If this all sounds too melodramatic and over-generalised, I do not apologise for it. My intension is to shake readers’ perception about the infallibility of “follow the standard” mantra. Enough money has been wasted on this so far. Do not trust “the experts” in a flowed concept any more – they do not know better than you do. Applying common sense to create your metadata will yield better outcomes than “following the standard” can ever deliver.
# A better approach - for better outcome
Now that you understand why “following the standard” is not a recipe for creating useful metadata records let’s review how to make it all work for you.
First and foremost, you have to define precisely WHY you need the metadata in the first place. The information you need to capture will differ depending whether the metadata is for internal use in data production and maintenance tasks or just to make the data easily discoverable by others.
For example, if you are building “just a catalogue” why bother with the complexity of ISO standard? Majority of users of your data simply want to know a few basic things about it:
As simple as that – nothing more and nothing less. Capture this information in a succinct metadata record and you have already done better than “following the standard” - even if this is only a paragraph of free text. And if you add a consistent structure to record that information for all you data you will achieve more than any expert in ISO 19115 standard can ever do for you.
If you are thinking that “most of these items are specified in the metadata standard anyway”, have a look at the documentation in detail. The key point is that most of these vital information items are either optional categories (so, may or may not be included in metadata record created “by the book”) or the choice of options in the mandatory categories does not allow including anything that is meaningful.
# Divide to conquer – metadata hierarchy
The key issue I have with ISO19115 metadata standard is that it tries to be all things to all people. The result is that it is too specific for most cataloguing purposes or not detailed enough for capturing really important details about the data for reuse, production and maintenance purposes. It also tries to be applicable to any spatial data which compounds its uselessness by bringing it all to a “least common denominator”. In reality, you have to capture and store different information for different data types and for different purposes, depending on the intended use of that information. Therefore, in a complex production environment you will need:
This or similar metadata hierarchy should be adopted to capture relevant information as data progresses through various stages of the production process. In an ideal environment you should maintain all that information and make it available for the end users of your data because it describes the product from end to end. Also, if you are engaged in a continuous production process, and that process changes over time, it is important to preserve all the relevant information for future perusal. However, for many, it will be an overkill as all they really need is the last metadata option, and in a very simplified format.
# Metadata granularity
In an ideal world there should be a metadata about every single piece of information you use or manage. In case of spatial data - about every single point, or line segment, or network node, or grid cell, as well as about their respective attributes. The hierarchy of metadata documentation outlined in the above section allows managing the granularity of maintained information. So, if you do need point/cell or segment metadata you can maintain that information in a lower level metadata construct (eg. production level metadata) and more generic information about your data can be captured in a higher level metadata. For example, information about source and acquisition date of a road segment data may be stored in production level metadata while your data licensing details in discovery level metadata. And one is linked to another via a hierarchy structure.
As you can see, this approach is flexible enough to allow storing relevant information about online applications as well as whole data collections with 100’s of layers but also about individual data layers, individual features within those layers, and down to the smallest spatial data construct – point/grid cell in space, if you need. You will never be able to achieve this level of granularity with ISO 19115 standard metadata.
# A word about naming your data/ spatial information
There are no conventions for naming spatial data that are universally acceptable and followed. Generally, creators try to give descriptive names and pack as much extra information into the title or file name as possible, so humans can quickly ascertain what the data is about by looking at just a file name or title. Data which is disseminated in “chunks”, like satellite imagery scenes or various grids structures, usually incorporate basic metadata information in their names - like satellite name, sensor, time of acquisition, resolution and grid/path references. This approach is handy if you need to interact with a small number of data files manually but it is a totally unnecessary complication if you have thousands of files. Your metadata should be a window to all your data and there should be no need to interact with the data via their convoluted naming convention. This is where the ISO 19115 metadata concept falls short again because it is inadequate for complex data filtering purposes, hence you have no choice but to interact with the data manually, based on file names, and not via purpose-built data query tools. That innovation could not happen to date.
For all practical purposes I suggest to stick to a minimum when naming your files. That is, giving your file a descriptive name and version/date id to make it unique. Information about everything else relating to you data should be in a proper metadata file.
# What about ISO19115 then?
You may still need to publish metadata in ISO 19115 standard, for example, to deal with limitations of cataloguing tools or to accommodate requirements of some of your less sophisticated clients. If you design your metadata content correctly, it will take just a few minutes for your programmer to map your metadata content to mandatory ISO categories and to make your metadata into an “ISO 19115 compliant” XML structure. The key point is to treat ISO metadata standard as an output format, one of many formats that may be required by different users, and not as a foundation for creating the metadata in the first place.
# Closing remarks
If you are wondering whether any of the above makes sense and why nobody else is raising the issue… Well, please consider this: those active in the OGC and spatial standards arena have quietly recognised the problem. There is already a number of initiatives on the way to develop more metadata standards for specific data formats (like Metadata for Observations and Measurements) and for “easier discovery” of spatial information (eg. Earth Observation extension of OpenSearch specification)… But true, no one has publicly admitted yet that the old approach failed and that the spatial community should have another go at solving metadata problem - in a holistic rather than piecemeal way.
Current approach to bring more and more standards is a lost cause as it is just an attempt to patch things up rather than to address the issue properly. Dividing and splitting the problem without acknowledging that it exists in the first place will only lead to more problems down the track - it will result in more chaos and confusion for the end users. These new initiatives are not about creating a hierarchy of metadata standards - just about more standards. If it was so difficult to successfully implement one standard, just imagine the troubles of trying to deal with 3 or more! The obvious question will be: “Which one do I use???” If you choose OpenSearch approach chances are that your data cannot be catalogued because traditional spatial cataloguing tools require ISO 19115 structure. And if your data formats happen to be different than “observations and measurements” or "image/grid", you may be waiting another decade for a proper standard to be published….
Persisting with the current approach to solve metadata problem will not succeed. As with Gordian Knot, there is only one way to solve this problem quickly…cut your loses short and start afresh. This is what I am doing. I will share my further thoughts and experiences in not too distant future.
Related Posts:
Why standards should be ignored
GIS metadata standard deficiencies
GIS standards dilemma
Ed Parsons on Spatial Data Infrastructure
Data overload makes SDI obsolete
# The history
Briefly about the anatomy of the problem (maybe a bit overdramatised but with a good intention to highlight where things went wrong). I remember vividly the presentation on spatial data interoperability I attended about a decade ago. One of the key messages was that metadata is “a glue that will allow it all to happen”. No problem with that but when I asked a few questions regarding practical implementation, I was quickly hushed. One of the presenters did approach me after the event and admitted that they were aware of the potential problems but that they did not want to alienate the audience by bringing those issues to the forefront at that point in time. As he put it, there were enough benefits in the proposed approach that warranted overlooking potential issues in order to get the maximum buy-in from key stakeholders. This spin approach succeeded. In years that followed you only got to hear about “good things” and quite a number of people made a career out of selling the “metadata success story” from one continent to the other. And the myth “just follow the standard and everything will be ok” perpetuated… never mind the truth.
# Background on metadata and standards
Let me set the record straight - I do not dismiss the need for metadata. On the contrary – metadata is a very, very important aspect of any data creation, maintenance and dissemination activity. But I question the usefulness of current metadata standards to support those tasks adequately.
“Standard” is just a set of conventions that a community of practice agrees to follow. It could be designed by a committee (pun intended) or arrived at by wide acceptance of a common practice (ie. when “someone’s way” of doing things is liked and followed by others). But standards should never be treated as a formula for success … Unfortunately, this is how ISO 19115 Geographic Information - Metadata standard has been sold to GIS community…
At a theoretical level, the concept of metadata is very simple. It is just “data about the data”… But this is where the simplicity ends and complexity begins because you quickly discover that metadata is useful not only for spatial data but also for spatial information in any format (ie. printed as well as electronic, single point to compilations of 100’s of layers, vectors as well as rasters and grids… and point clouds… one-off or dynamically generated on the fly… and whatever else you want to class as “spatial”).
# Why metadata implementation projects fail
A typical metadata implementation project goes like this - an organisation accumulates more and more data, to the extent that it causes problems for the IT department to manage it. So, a project is initiated to “catalogue what we have”. And in order to catalogue systematically what you have you need to describe it in a consistent way - you need metadata. The obvious step is to search if there are any standards that will help to deal with the issue rather than reinventing the wheel.
It does not take much effort to find the spatial data metadata standard documentation. So, you start reading and quickly realise that you cannot make any sense of the gobbledygook of the official documentation. A thought inevitably crosses your mind - “I need an expert!”. And of course you look for… “the expert in ISO 19115 metadata standard” . In the end, you get what you ask for - an expert in the standard, not the expert in solving your kind of problems. That expert cannot advise you otherwise but just to “follow the standard and you will be right”.
The expert usually brings a set of recommend tools (which of course are built around ISO metadata standard) and you also get help in implementing your catalogue/system “by the book”. All good, great success… Until you realise that a classic “garbage in / garbage out” principle applies here as well… what a surprise!
You see, the failure is built into the solution (it is the metadata standard!) so, it is very rare that this approach delivers. You don’t believe me? Just talk to those who have to use the information contained in metadata records (not those who implement metadata standards and build systems!)…
Ok, to be fair, there is one exception where you can achieve an acceptable outcome (but I caveat this statement depending on how you define “acceptable”). It will happen only if you rule with an iron fist what information goes into your metadata. However, it can get out of hands easily if you have a lot of data to deal with (so you take shortcuts to process it all in bulk), a lot of different people writing metadata content (so, there are different views of what is important and needs to be recorded – because the standard allows this), and/or a lot of different types of data or data that grows rapidly (lack of consistency of information or sheer volume of data makes it impossible to record all the useful details). And let me stress this again, there is no guarantee that content of your metadata will be of any use outside of your organisation or immediate community of interest because it may lack the information perceived by others as vital (see my earlier post for more explanations).
If this all sounds too melodramatic and over-generalised, I do not apologise for it. My intension is to shake readers’ perception about the infallibility of “follow the standard” mantra. Enough money has been wasted on this so far. Do not trust “the experts” in a flowed concept any more – they do not know better than you do. Applying common sense to create your metadata will yield better outcomes than “following the standard” can ever deliver.
# A better approach - for better outcome
Now that you understand why “following the standard” is not a recipe for creating useful metadata records let’s review how to make it all work for you.
First and foremost, you have to define precisely WHY you need the metadata in the first place. The information you need to capture will differ depending whether the metadata is for internal use in data production and maintenance tasks or just to make the data easily discoverable by others.
For example, if you are building “just a catalogue” why bother with the complexity of ISO standard? Majority of users of your data simply want to know a few basic things about it:
- what is it (basic description – including list of features for compilation products, spatial accuracy, geographic extents, when created/time reference and version if more than one created),
- where to get it from and how (online or shopfront, order hardcopy or download electronic format),
- how much it costs (free or paid - how much!),
- how to access it (ie. access constraint, e.g. “none” or “login” or “restricted” plus relevant classification level) and what can be done with it (eg. “internal use only”, or “non commercial use only” or “republish with attribution”, etc.) – important to separate the two to make it clear!
As simple as that – nothing more and nothing less. Capture this information in a succinct metadata record and you have already done better than “following the standard” - even if this is only a paragraph of free text. And if you add a consistent structure to record that information for all you data you will achieve more than any expert in ISO 19115 standard can ever do for you.
If you are thinking that “most of these items are specified in the metadata standard anyway”, have a look at the documentation in detail. The key point is that most of these vital information items are either optional categories (so, may or may not be included in metadata record created “by the book”) or the choice of options in the mandatory categories does not allow including anything that is meaningful.
# Divide to conquer – metadata hierarchy
The key issue I have with ISO19115 metadata standard is that it tries to be all things to all people. The result is that it is too specific for most cataloguing purposes or not detailed enough for capturing really important details about the data for reuse, production and maintenance purposes. It also tries to be applicable to any spatial data which compounds its uselessness by bringing it all to a “least common denominator”. In reality, you have to capture and store different information for different data types and for different purposes, depending on the intended use of that information. Therefore, in a complex production environment you will need:
- Metadata describing source inputs (ie. to define lineage of your data);
- Metadata for production datasets (which describes interim data versions at various stages of the process of transformation of inputs/source data into finished product);
- Metadata for all output formats of the finished product (since it is inevitable that format conversion will alter the data in some way so, it needs to be documented that “what you see” eg. on a slippy map demonstrating the data is not the same as what you get in a data file in format x and which is different yet to that in format y; this is due to generalisations and other inherent alterations of original inputs in the process of spatial conversion);
- Metadata for discovery (ie. solely for cataloguing purposes).
This or similar metadata hierarchy should be adopted to capture relevant information as data progresses through various stages of the production process. In an ideal environment you should maintain all that information and make it available for the end users of your data because it describes the product from end to end. Also, if you are engaged in a continuous production process, and that process changes over time, it is important to preserve all the relevant information for future perusal. However, for many, it will be an overkill as all they really need is the last metadata option, and in a very simplified format.
# Metadata granularity
In an ideal world there should be a metadata about every single piece of information you use or manage. In case of spatial data - about every single point, or line segment, or network node, or grid cell, as well as about their respective attributes. The hierarchy of metadata documentation outlined in the above section allows managing the granularity of maintained information. So, if you do need point/cell or segment metadata you can maintain that information in a lower level metadata construct (eg. production level metadata) and more generic information about your data can be captured in a higher level metadata. For example, information about source and acquisition date of a road segment data may be stored in production level metadata while your data licensing details in discovery level metadata. And one is linked to another via a hierarchy structure.
As you can see, this approach is flexible enough to allow storing relevant information about online applications as well as whole data collections with 100’s of layers but also about individual data layers, individual features within those layers, and down to the smallest spatial data construct – point/grid cell in space, if you need. You will never be able to achieve this level of granularity with ISO 19115 standard metadata.
# A word about naming your data/ spatial information
There are no conventions for naming spatial data that are universally acceptable and followed. Generally, creators try to give descriptive names and pack as much extra information into the title or file name as possible, so humans can quickly ascertain what the data is about by looking at just a file name or title. Data which is disseminated in “chunks”, like satellite imagery scenes or various grids structures, usually incorporate basic metadata information in their names - like satellite name, sensor, time of acquisition, resolution and grid/path references. This approach is handy if you need to interact with a small number of data files manually but it is a totally unnecessary complication if you have thousands of files. Your metadata should be a window to all your data and there should be no need to interact with the data via their convoluted naming convention. This is where the ISO 19115 metadata concept falls short again because it is inadequate for complex data filtering purposes, hence you have no choice but to interact with the data manually, based on file names, and not via purpose-built data query tools. That innovation could not happen to date.
For all practical purposes I suggest to stick to a minimum when naming your files. That is, giving your file a descriptive name and version/date id to make it unique. Information about everything else relating to you data should be in a proper metadata file.
# What about ISO19115 then?
You may still need to publish metadata in ISO 19115 standard, for example, to deal with limitations of cataloguing tools or to accommodate requirements of some of your less sophisticated clients. If you design your metadata content correctly, it will take just a few minutes for your programmer to map your metadata content to mandatory ISO categories and to make your metadata into an “ISO 19115 compliant” XML structure. The key point is to treat ISO metadata standard as an output format, one of many formats that may be required by different users, and not as a foundation for creating the metadata in the first place.
# Closing remarks
If you are wondering whether any of the above makes sense and why nobody else is raising the issue… Well, please consider this: those active in the OGC and spatial standards arena have quietly recognised the problem. There is already a number of initiatives on the way to develop more metadata standards for specific data formats (like Metadata for Observations and Measurements) and for “easier discovery” of spatial information (eg. Earth Observation extension of OpenSearch specification)… But true, no one has publicly admitted yet that the old approach failed and that the spatial community should have another go at solving metadata problem - in a holistic rather than piecemeal way.
Current approach to bring more and more standards is a lost cause as it is just an attempt to patch things up rather than to address the issue properly. Dividing and splitting the problem without acknowledging that it exists in the first place will only lead to more problems down the track - it will result in more chaos and confusion for the end users. These new initiatives are not about creating a hierarchy of metadata standards - just about more standards. If it was so difficult to successfully implement one standard, just imagine the troubles of trying to deal with 3 or more! The obvious question will be: “Which one do I use???” If you choose OpenSearch approach chances are that your data cannot be catalogued because traditional spatial cataloguing tools require ISO 19115 structure. And if your data formats happen to be different than “observations and measurements” or "image/grid", you may be waiting another decade for a proper standard to be published….
Persisting with the current approach to solve metadata problem will not succeed. As with Gordian Knot, there is only one way to solve this problem quickly…cut your loses short and start afresh. This is what I am doing. I will share my further thoughts and experiences in not too distant future.
Related Posts:
Why standards should be ignored
GIS metadata standard deficiencies
GIS standards dilemma
Ed Parsons on Spatial Data Infrastructure
Data overload makes SDI obsolete
Tuesday, September 24, 2013
South Australia opens its data
South Australian government is the latest convert to free and open data cause. Unveiling www.data.sa.gov.au portal South Australian premier, Jay Weatherill, mandated that all state government agencies are to house their public data in a central portal to ensure that it is accessible to the community at large. At present there are 229 data sets released by a number of SA state government agencies including the Department of Planning, Transport and Infrastructure and the Attorney-General’s Department.
As with all the other initiatives of similar type, the success will be measured by uptake of released data by business and the community. However, this information is hard to compile, so my litmus test of the likely success of a particular “data.gov.au” initiative is how much of useful information is put in the public domain...
It looks that SA is on a right track releasing full roads dataset but more spatial data has to be made available in order for this initiative to start paying off for the effort involved. My next criticism is rather useless (or frankly, lacking) metadata information for supplied data but this issue is not unique to SA and other jurisdictions are also guilty of neglecting that aspect of the "data discovery" part of their respective project. "ISO 19115 - Geographic Information Metadata" is certainly nowhere to be seen on "data.gov.au" portals...
Below is a quick scorecard of State and Federal government open data initiatives - based on availability of “high value” spatial data (as per my very subjective list).
Table. Availability of Free Fundamental Spatial Data
I will restrain from providing my assessment of those initiatives at this point in time. It is enough to say that expectations are high as to the economic value free and open data could deliver but, as you can see from the above matrix, there are many gaps in availability of what I consider fundamental data to make any meaningful impact… Let's give it a year and see if there are any improvements.
Related Posts:
East coast unanimously frees data
Free data a GFC casualty
Governments intensify free data efforts
Data overload makes SDI obsolete
What’s the benefit of gov data warehouses?
First spotted on: spatialsource.com.au
As with all the other initiatives of similar type, the success will be measured by uptake of released data by business and the community. However, this information is hard to compile, so my litmus test of the likely success of a particular “data.gov.au” initiative is how much of useful information is put in the public domain...
It looks that SA is on a right track releasing full roads dataset but more spatial data has to be made available in order for this initiative to start paying off for the effort involved. My next criticism is rather useless (or frankly, lacking) metadata information for supplied data but this issue is not unique to SA and other jurisdictions are also guilty of neglecting that aspect of the "data discovery" part of their respective project. "ISO 19115 - Geographic Information Metadata" is certainly nowhere to be seen on "data.gov.au" portals...
Below is a quick scorecard of State and Federal government open data initiatives - based on availability of “high value” spatial data (as per my very subjective list).
Table. Availability of Free Fundamental Spatial Data
Dataset
|
Fed
|
ACT
|
NSW
|
NT
|
Qld
|
SA
|
Tas
|
Vic
|
WA
|
Gazetteer
|
Yes
|
Yes
|
Yes
|
||||||
Cadastre boundaries
|
Yes (by LGA)
|
Adelaide
City only
|
Yes
|
||||||
Addresses locations
|
Gungahlin
Town Centre only
|
Yes (as a list)
|
Yes
|
||||||
Roads
|
Yes (State managed only)
|
Yes
|
Yes
|
||||||
Admin boundaries
|
Yes (via ABS)
|
Some State specific
|
Yes
|
||||||
Property sales
|
By LGA only
|
?
|
|||||||
Property/ land valuations
|
Yes (data at LGA level only)
|
?
|
|||||||
Elevation
|
Yes
(30m)
|
Yes (90% coverage at 10m)
|
Yes (contours 1m+)
|
||||||
High Res imagery
|
25-15m
Landsat; 2.5m AGRI
|
Potentially (as tile service)
|
Old Landsat imagery
|
||||||
Overall
|
I will restrain from providing my assessment of those initiatives at this point in time. It is enough to say that expectations are high as to the economic value free and open data could deliver but, as you can see from the above matrix, there are many gaps in availability of what I consider fundamental data to make any meaningful impact… Let's give it a year and see if there are any improvements.
Related Posts:
East coast unanimously frees data
Free data a GFC casualty
Governments intensify free data efforts
Data overload makes SDI obsolete
What’s the benefit of gov data warehouses?
First spotted on: spatialsource.com.au
Thursday, June 6, 2013
East coast unanimously frees data
The NSW state government has joined QLD, Vic and ACT counterparts declaring support for open data initiative. It has just released a draft open data policy which aims to make state-owned data available to the public at no charge. Where is it not possible to provide data totally free of charge, it will be provided “at a reasonable price”. If the policy is approved, datasets will be available for download from www.data.nsw.gov.au portal.
If declarations of state and territory governments prove fruitful, we soon should have roads and cadastre/address data freely available for the eastern part of Australia - unless I totally misinterpret the intension of the open data policies. The move to free access to government data started 12 years ago with the release of Spatial Data Access and Pricing Policy however, the benefits have been slow coming. Hopefully, the momentum will now accelerate as more and more government organisations open up their data vaults.
Govhack 2013 initiative gives a taste of what can be done with all that free data. The winners have just been announced…
First spotted on Spatialsource.com.au
If declarations of state and territory governments prove fruitful, we soon should have roads and cadastre/address data freely available for the eastern part of Australia - unless I totally misinterpret the intension of the open data policies. The move to free access to government data started 12 years ago with the release of Spatial Data Access and Pricing Policy however, the benefits have been slow coming. Hopefully, the momentum will now accelerate as more and more government organisations open up their data vaults.
Govhack 2013 initiative gives a taste of what can be done with all that free data. The winners have just been announced…
First spotted on Spatialsource.com.au
Thursday, May 30, 2013
Online map competition intensifies
There was a time that Google and its Map API was the only talk in town. Their revolutionary approach to serving spatial data online has broken the monopoly of specialist GIS companies on all things spatial. Many followed in Google footsteps with similar, simplified online products but were mostly overshadowed by the first mover in the field. For a moment it seemed that nothing else matters and that Google will dominate the market indefinitely. However, introduction of fees for high usage of Google Map API led to a big backlash against the company as many developers realised the vulnerability of their business to caprices of a key partner. Suddenly alternatives started to look very attractive - despite the limited functionality offered. And the competition in the online map market started to intensify again...
Acquisition of Waze by Facebook for a reported $1B is the flavour of the month (although not done deal yet). It demonstrates that the appetite of large companies for ready-made online mapping technologies is growing. Apple has its fingers firmly in the pie beefing up reliability of its offering (after a very unfortunate start) and optimising performance with vector graphics far beyond Google Map capabilities. Microsoft is surprisingly quiet…
The key enabler for “lower end” alternatives to Google Map was, and still is, Open Street Map project which provided developers with roads dataset all over the world (traditionally, the cost of using that data was so restrictive that only those with very deep pockets could attempt to do it on a world scale). CloudMade and MapQuest offered free access to their versions of OSM data and started building a variety of free services, enabling replicating key functionality of Google Map, such as geocoding or navigation. ESRI has also thrown in their support releasing free map layers and their GeoCommons platform (although I am not sure if the current licensing restrictions still allow unconstrained use of that data).
There are many viable alternatives to Google Map and, as a developer or end user, you are no longer forced to accept strict conditions imposed by Google. By switching to any of those alternatives you will have much more flexibility in developing services and applications and will take back control of your business. And you don’t have to compromise on functionality, at least not too much… My favourite amongst free alternatives is Leafletjs (originally developed by CloudMade). It is easily extendible with free services from MapQuest. If you are after a 3D kind, Cesium from Analytical Graphics, Inc looks very promising - supports also 2D mapping but unfortunately, it works only in WebGL enabled browsers (by the way, NICTA built on this platform a very impressive range of spatial applications which demonstrate its full potential).
Acquisition of Waze by Facebook for a reported $1B is the flavour of the month (although not done deal yet). It demonstrates that the appetite of large companies for ready-made online mapping technologies is growing. Apple has its fingers firmly in the pie beefing up reliability of its offering (after a very unfortunate start) and optimising performance with vector graphics far beyond Google Map capabilities. Microsoft is surprisingly quiet…
The key enabler for “lower end” alternatives to Google Map was, and still is, Open Street Map project which provided developers with roads dataset all over the world (traditionally, the cost of using that data was so restrictive that only those with very deep pockets could attempt to do it on a world scale). CloudMade and MapQuest offered free access to their versions of OSM data and started building a variety of free services, enabling replicating key functionality of Google Map, such as geocoding or navigation. ESRI has also thrown in their support releasing free map layers and their GeoCommons platform (although I am not sure if the current licensing restrictions still allow unconstrained use of that data).
There are many viable alternatives to Google Map and, as a developer or end user, you are no longer forced to accept strict conditions imposed by Google. By switching to any of those alternatives you will have much more flexibility in developing services and applications and will take back control of your business. And you don’t have to compromise on functionality, at least not too much… My favourite amongst free alternatives is Leafletjs (originally developed by CloudMade). It is easily extendible with free services from MapQuest. If you are after a 3D kind, Cesium from Analytical Graphics, Inc looks very promising - supports also 2D mapping but unfortunately, it works only in WebGL enabled browsers (by the way, NICTA built on this platform a very impressive range of spatial applications which demonstrate its full potential).
Tuesday, May 14, 2013
Rift in GIS ranks
There is a troubling development in GIS community that threatens to flare up old animosities and split participants along the lines of big versus small as well as proprietary versus open source. In the centre of it are Open Geospatial Consortium (OGC) and its members, representing open source camp, minor proprietary solutions vendors and 800 pound gorilla in GIS – ESRI. Ultimately, the whole incident can undermine, one way or another, OGC stronghold on all things spatial…
Firstly, a short history of OGC, from my perspective. In the beginning OGC was just an obscure “public benefit” organisation focused on developing and promoting open and interoperable standards to facilitate exchange of spatial information. There was a real problem that required urgent attention – that is, finding an easy way to exchange vast amounts of valuable spatial data stored in “walled gardens” and in obscure proprietary formats. So, many were prepared to put a lot of effort in their spare as well as “employer paid time” in order to come up with a solution to this problem. This early period attracted a lot of prominent participants from government organisations and academia across the world, as well as support (often financial) from several industry sectors hoping for significant cost savings in the future. It was a community wide initiative with roots firmly established in an open source movement.
When OGC standards started gaining traction and wider acceptance amongst key buyers of GIS technologies the organisation attracted to the cause members from a proprietary solutions camp as well. OGC reached then the peak of its relevance. A new era started where a united GIS community worked together towards a common goal – achieving full interoperability of spatial data, regardless whether data originated in proprietary or open source formats, or using proprietary or open source tools.
However, the relevance of OGC and its initiatives is under threat now - from outsiders as rapid technological advancement demands more flexible, easier to implement and performance focused standards (like those developed by organisations and individuals operating on fringes of GIS - Open Street Map initiative and GeoJSON are the two most prominent examples), but also from within its own ranks as the organisation is forced to deal with conflicting interests of its members.
In particular, ESRI had put forward a new GeoSpatial REST API standard for endorsement by OGC. It covers a wide set of web services including catalogue, map, feature, imagery, geometry, geoprocessing, and geocoding web services. However, a significant number of OGC members lodged an objection to it and demand the documentation not to be adopted as an official OGC standard.
The essence of the objection is that the proposed standard is not a community designed scheme but rather just a documentation of an implementation of proprietary web services by a single commercial vendor. No other vendors will be able to build solutions against that standard, and if endorsed, it will dilute relevance of existing OGC standards. When it becomes an OGC endorsed standard there is a danger that buyers will demand that all the vendors of spatial solutions support that standard in their products, hence giving an unfair advantage in the market to vendors of ESRI solutions. The objections are fully documented on OSGeo website.
Unless a workable compromise can be found, then regardless whichever way OGC decision goes it will alienate a significant group of its members and ultimately, it will split GIS community. That is, it will upset either open source and non-ESRI vendors or ESRI and thousands of its affiliates whose livelihood is almost exclusively dependent on ESRI’s fortunes. If things get rough ESRI is dominant enough in the market to go its own way, without the blessing from OGC. But will OGC be able to retain its relevance without such a prominent member as ESRI? Potentially some troubling times ahead for GIS industry...
[Update: ESRI has withdrawn the GeoSpatial REST API standard from OGC, potentially defusing the situation. ]
Firstly, a short history of OGC, from my perspective. In the beginning OGC was just an obscure “public benefit” organisation focused on developing and promoting open and interoperable standards to facilitate exchange of spatial information. There was a real problem that required urgent attention – that is, finding an easy way to exchange vast amounts of valuable spatial data stored in “walled gardens” and in obscure proprietary formats. So, many were prepared to put a lot of effort in their spare as well as “employer paid time” in order to come up with a solution to this problem. This early period attracted a lot of prominent participants from government organisations and academia across the world, as well as support (often financial) from several industry sectors hoping for significant cost savings in the future. It was a community wide initiative with roots firmly established in an open source movement.
When OGC standards started gaining traction and wider acceptance amongst key buyers of GIS technologies the organisation attracted to the cause members from a proprietary solutions camp as well. OGC reached then the peak of its relevance. A new era started where a united GIS community worked together towards a common goal – achieving full interoperability of spatial data, regardless whether data originated in proprietary or open source formats, or using proprietary or open source tools.
However, the relevance of OGC and its initiatives is under threat now - from outsiders as rapid technological advancement demands more flexible, easier to implement and performance focused standards (like those developed by organisations and individuals operating on fringes of GIS - Open Street Map initiative and GeoJSON are the two most prominent examples), but also from within its own ranks as the organisation is forced to deal with conflicting interests of its members.
In particular, ESRI had put forward a new GeoSpatial REST API standard for endorsement by OGC. It covers a wide set of web services including catalogue, map, feature, imagery, geometry, geoprocessing, and geocoding web services. However, a significant number of OGC members lodged an objection to it and demand the documentation not to be adopted as an official OGC standard.
The essence of the objection is that the proposed standard is not a community designed scheme but rather just a documentation of an implementation of proprietary web services by a single commercial vendor. No other vendors will be able to build solutions against that standard, and if endorsed, it will dilute relevance of existing OGC standards. When it becomes an OGC endorsed standard there is a danger that buyers will demand that all the vendors of spatial solutions support that standard in their products, hence giving an unfair advantage in the market to vendors of ESRI solutions. The objections are fully documented on OSGeo website.
Unless a workable compromise can be found, then regardless whichever way OGC decision goes it will alienate a significant group of its members and ultimately, it will split GIS community. That is, it will upset either open source and non-ESRI vendors or ESRI and thousands of its affiliates whose livelihood is almost exclusively dependent on ESRI’s fortunes. If things get rough ESRI is dominant enough in the market to go its own way, without the blessing from OGC. But will OGC be able to retain its relevance without such a prominent member as ESRI? Potentially some troubling times ahead for GIS industry...
[Update: ESRI has withdrawn the GeoSpatial REST API standard from OGC, potentially defusing the situation. ]
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