The mass of the Antarctic ice sheet has changed over the last several years. Research based on observations from NASA’s twin NASA/German Aerospace Center’s twin Gravity Recovery and Climate Experiment (GRACE) satellites indicates that between 2002 and 2016, Antarctica shed approximately 125 gigatons of ice per year, causing global sea level to rise by 0.35 millimeters per year. These images, created with GRACE data, show changes in Antarctic ice mass since 2002. Orange and red shades indicate areas that lost ice mass, while light blue shades indicate areas that gained ice mass. White indicates areas where there has been very little or no change in ice mass since 2002. In general, areas near the center of Antarctica experienced small amounts of positive or negative change, while the West Antarctic Ice Sheet experienced a significant ice mass loss (dark red) over the fourteen-year period. Floating ice shelves whose mass GRACE doesn't measure are colored gray. GRACE Mission: 15 Years of Watching Water on Earth Iridium Will Share Falcon 9 Launch With GRACE-FO GRACE-FO to Share Ride into Orbit Analyzing Recent Trends in U.S. Flood Risk First GRACE Follow-On Satellite Completes Construction Australian Continent Shifts With Seasons

The biology and conservation of elephants THE symbol of the World Wide Fund for Nature is a giant panda. The panda’s black-and-white pelage certainly makes for a striking logo. But, though pandas are an endangered species, the cause of their endangerment is depressingly quotidian: a loss of habitat as Earth’s human population increases. A better icon might be an elephant, particularly an African elephant, for elephants are not mere collateral damage in humanity’s relentless expansion. Often, rather, they are deliberate targets, shot by poachers, who want their ivory; by farmers, because of the damage they do to crops; and by cattle herders, who see them as competitors for forage. In August 2016 the result of the Great Elephant Census, the most extensive count of a wild species ever attempted, suggested that about 350,000 African savannah elephants remain alive. This is down by 140,000 since 2007. The census, conducted by a team led by Mike Chase, an ecologist based in Botswana, and paid for by Paul Allen, one of the founders of Microsoft, undertook almost 500,000km of aerial surveys to come to its conclusion—though the team were unable to include forest elephants, a smaller, more reclusive type that live in west and central Africa, and which many biologists think a separate species. That most of the decline has been brought about by poaching is scarcely in doubt. Seizures of smuggled ivory, and the size of the carved-ivory market compared with the small amount of legal ivory available, confirm it. But habitat loss is important, too—and not just the conversion of bush into farmland. Roads, railways and fences, built as Africa develops, stop elephants moving around. And an elephant needs a lot of room. According to George Wittemyer of Save the Elephants (STE), a Kenyan research-and-conservation charity, an average elephant living in and around Samburu National Reserve, in northern Kenya, ranges over 1,500 square kilometres during the course of a year, and may travel as much as 60km a day. The long road to knowledge The question, then, is whether elephants and people can ever co-exist peacefully. And many of those who worry that the answer may be “no” fear the loss of more than just another species of charismatic megafauna. Elephants, about as unrelated to human beings as any mammal can be, seem nevertheless to have evolved intelligence, and possibly even consciousness. Though they may not be alone in this (similar claims are made for certain whales, social carnivores and a few birds), they are certainly part of a small and select group. Losing even one example of how intelligence comes about and makes its living in the wild would not only be a shame in its own right, it would also diminish the ability of biologists of the future to understand the process, and thus how it happened to human beings. Most of what is known about elephant society has been found out by STE’s study in Samburu and by an even longer-running project, led by Cynthia Moss, at Amboseli National Park, in the country’s south. Both use a mixture of good, old-fashioned fieldcraft and high-tech radio collars that permit individual animals to be tracked around by satellite. Dr Moss began her work in Amboseli in 1972, after collaborating in Tanzania with Iain Douglas-Hamilton, a zoologist who had been studying the animals since 1965 (and who is, coincidentally, the uncle of our Books and Arts editor). In 1993 Dr Douglas-Hamilton, who had held various conservation-related jobs in the interim, followed suit by creating STE and recruiting Dr Wittemyer to set up a research project in Samburu. That project now monitors 70 family groups comprising about 300 adult females and their offspring, and also around 200 adult males. Since they began work, Dr Wittemyer and his team have collected more than 25,000 field observations of what the animals are up to, and around 4m individual satellite locations. Dr Wittemyer argues that, human beings aside, no species on Earth has a more complex society than that of elephants. And elephant society does indeed have parallels with the way humans lived before the invention of agriculture. The nuclei of their social arrangements are groups of four or five females and their young that are led by a matriarch who is mother, grandmother, great-grandmother, sister or aunt to most of them. Though males depart their natal group when maturity beckons at the age of 12, females usually remain in it throughout their lives. Within a group, most adult females have, at any given moment, a single, dependent calf. They will not give birth again until this offspring is self-sufficient, which takes about four years. From a male point of view, sexually receptive females are therefore a rare commodity, to be sought out and often fought over. Such competition means that, though capable of fatherhood from the age of about 14, a male will be lucky to achieve it before he is in his 20s. Until that time arrives, he will be seen off by stronger rivals. Were this all there was to elephant society, it would still be quite complex by mammalian standards—similar in scope to that of lions, which also live in matriarchal family groups that eject maturing males. But it would not deserve Dr Wittemyer’s accolade of near-human sophistication. Unlike lions, however, elephants have higher levels of organisation, not immediately obvious to the observer, that are indeed quite humanlike. First of all, families are part of wider “kinship” groups that come together and separate as the fancy takes them. Families commune with each other in this way about 10% of the time. On top of this, each kinship group is part of what Dr Douglas-Hamilton, a Scot, calls a clan. Clans tend to gather in the dry season, when the amount of habitat capable of supporting elephants is restricted. Within a clan, relations are generally friendly. All clan members are known to one another and, since a clan will usually have at least 100 adult members, and may have twice that, this means an adult (an adult female, at least) can recognise and have meaningful social relations with that many other individuals. A figure of between 100 and 200 acquaintances is similar to the number of people with whom a human being can maintain a meaningful social relationship—a value known as Dunbar’s number, after Robin Dunbar, the psychologist who proposed it. Dunbar’s number for people is about 150. It is probably no coincidence that this reflects the maximum size of the human clans of those who make their living by hunting and gathering, and who spend most of their lives in smaller groups of relatives, separated from other clan members, scouring the landscape for food. Dealing with so many peers, and remembering details of such large ranges, means elephants require enormous memories. Details of how their brains work are, beyond matters of basic anatomy, rather sketchy. But one thing which is known is that they have big hippocampuses. These structures, one in each cerebral hemisphere, are involved in the formation of long-term memories. Compared with the size of its brain, an elephant’s hippocampuses are about 40% larger than those of a human being, suggesting that the old proverb about an elephant never forgetting may have a grain of truth in it. À la recherche du temps perdu In the field, the value of the memories thus stored increases with age. Matriarchs, usually the oldest elephant in a family group, know a lot. The studies in Amboseli and Samburu have shown that, in times of trouble such as a local drought, this knowledge permits them to lead their groups to other, richer pastures visited in the past. Though not actively taught (at least, as far as is known) such geographical information is passed down the generations by experience. Indeed, elephant biologists believe the ability of the young to benefit by and learn from the wisdom of the old is one of the most important reasons for the existence of groups—another thing elephants share with people. Group living brings further advantages, as well—most notably those of collective defence. For, though most predators apart from humans armed with rifles would hesitate to attack an adult elephant, they will happily take on a youngster. A lone mother would be able to defend her calf against a single such predator, but many carnivores, particularly lions and hyenas, come in prides or packs. The solidarity of sisterhood means a group of elephants can usually deter attacks by its mere existence, and if deterrence does not work, then collective defence usually does. Here, again, experience seems to count. Data collected by Dr Moss’s team suggest that groups led by young matriarchs are more vulnerable to predation than those with older leaders. Nor is it only in their social arrangements that elephants show signs of parallel evolution with humans. They also seem to have a capacity for solving problems by thinking about them in abstract terms. This is hard to demonstrate in the wild, for any evidence is necessarily anecdotal. But experiments conducted on domesticated Asian elephants (easier to deal with than African ones) show that they can use novel objects as tools to obtain out-of-reach food without trial and error beforehand. This is a trick some other species, such as great apes, can manage, but which most animals find impossible. Wild elephants engage in one type of behaviour in particular that leaves many observers unable to resist drawing human parallels. This is their reaction to their dead. Elephant corpses are centres of attraction for living elephants. They will visit them repeatedly, sniffing them with their trunks and rumbling as they do so (see picture overleaf). This is a species-specific response; elephants show no interest in the dead of any other type of animal. And they also react to elephant bones, as well as bodies, as Dr Wittemyer has demonstrated. Prompted by the anecdotes of others, and his own observations that an elephant faced with such bones will often respond by scattering them, he laid out fields of bones in the bush. Wild elephants, he found, can distinguish their conspecifics’ skeletal remains from those of other species. And they do, indeed, pick them up and fling them into the bush. Elephants, then, are of great scientific curiosity. But, as its name suggests, Save the Elephants was not set up solely for the disinterested pursuit of knowledge. Indeed, as has often proved the way in field studies of other species, the focus of almost all elephant researchers, not just those in Kenya, has shifted from understanding the animals to preserving them. Though poaching is still a threat in Kenya, changes in land use now seem an equal hazard. The human inhabitants of the area around the Samburu reserve (some of whom have given their tribal name to the place) have traditionally made their livings as pastoralists, driving herds of cattle from grazing place to grazing place. One source of conflict with elephants has been competition for pasture as the herders’ populations have grown. Indeed, the reserve itself is now sometimes invaded by cowherds and their stock. But, on top of this, some pastoralists have begun to settle down. Buildings and fences are appearing on land which, though outside the reserve, is part of the local elephants’ ranges as they travel from one place to another. Here, the data Dr Wittemyer and his team have accumulated can help. Satellite tracking that shows exactly how elephants move about (see map) can be used to steer decisions concerning land use in ways that help pachyderms. As the map shows, elephants have places they prefer to live, which often correspond to protected areas, for the animals quickly work out where they are safe and where they are not. When travelling between these, which they usually do at night, they often follow narrow corridors. Bee off with you Keeping such corridors clear of development is crucial to the well-being of the elephants which use them. Satellite maps are an important tool for doing so. Formal authorities in the country can take them into account, but, equally important, these maps are also quite persuasive in the public meetings at which local tribesmen agree on the use of what is collectively held land. Such meetings can assent to the legal “gazetting” of the corridors in question, to stop them being built on or fenced, so that elephants can pass freely. This approach can work at a larger scale, as well. A new railway from Mombasa to Nairobi, for example, has been provided with elephant underpasses on routes used by the beasts—though an unintended consequence has been to encourage settlement near these transit points, which are useful for people, too. In the case of Samburu the satellite maps will be of great value if a proposed “development corridor”, running inland from a planned expansion of the port of Lamu, goes ahead, as this may bring a new highway, railway and oil pipeline through land much used by elephants. Understanding elephants’ behaviour also permits it to be manipulated in ways that help reduce direct conflict between elephants and people. One such project harnesses elephants’ fear of bee swarms. Bees are the only animals apart from humans that elephants seem truly afraid of. Anecdotally, this has been known for a long time. But the matter has now been studied scientifically by Lucy King, a researcher at Oxford University who is also part of STE. Dr King proved the anecdotes correct by playing the sound of a swarm of angry bees to wild elephants, and videoing the instant, panicked flight it provoked. The reason for this panic is that, although a bee’s sting cannot penetrate most parts of an elephant’s hide, swarms of bees tend to go for the eyes and the tip of the trunk, a pachyderm’s most vulnerable parts. Bees are enemies that no amount of collective defence can discourage. Armed with that knowledge, Dr King and her colleague Fritz Vollrath came up with the idea of protecting farms with bee fences. The sort of fence most Kenyan smallholders can afford is too flimsy to exclude an elephant. But a bee fence, though flimsier still, does the job. It consist of pairs of poles about three metres apart, between which beehives can be hung like hammocks. The hives themselves are ten metres apart, and the poles are all connected by a single strand of wire 1.5 metres above the ground. This arrangement is enough to stop elephants in their tracks. Most are sufficiently wary of hives to avoid passing the fence in the first place—indeed, they are so wary that half the hives can be cheap dummies, rather than the real thing, without reducing a fence’s effectiveness. Those that do try to pass between the poles blunder into the wire and shake the adjacent hives, with predictable results, and rarely attempt a second passage. Bee-fenced farms, Dr King and Dr Vollrath have discovered, suffer only a fifth as many elephant raids as those with conventional protection. As a bonus, the honey the bees produce is a useful source of revenue. Indeed, the fences are so successful that they are being tried out in at least a dozen other countries. Though it seems almost a Heath-Robinson solution to the problem, bee fencing may be an important part of reconciling the interests of elephants and people. Jumbo threat All the bee fences in the world, however, will not help if the problem of poaching remains unsolved. And that, ultimately, means suppressing demand for ivory. For years this looked a fool’s errand. Now, though, it does not, for good news has arrived from what many regard as an unexpected quarter: the government of China. Though international trade in ivory is illegal, some countries permit internal sales—and do not always inquire too closely about where the tusks contributing to those sales have come from. In recent years China, which has permitted such sales, has been the world’s largest ivory market, estimated to account for 70% of ivory sold. By the end of 2017, though, any sale of ivory in China will be illegal, and all licensed ivory dealers will have had to shut up shop. The Chinese do seem serious about this. Not only are dealers actually closing down, but an anti-ivory propaganda campaign has begun, with stars such as Yao Ming, a basketball player, and Li Bingbing, an actress, being recruited to shame those who continue to buy objects made from elephant tusks. Though there is evidence of new workshops opening, and others expanding, in some of China’s neighbours such as Vietnam, many people hope that China’s ivory ban will prove a tipping-point in the fight to preserve elephants. Already, the price of the stuff in China has come down by two-thirds, from a peak of $2,100 a kilogram in 2014 to $730 earlier this year. That is bad news for smugglers, and for the poachers who supply them. If the Chinese ban really does stick, rather than driving the trade underground, then it is just possible that historians of the future will record 2017 as having been the year of the elephant. Reuse this content

The NYC Taxi and Limousine Commission (TLC) has publicly released a dataset of taxi trips from January 2009 — June 2016 with GPS coordinates for starting and endpoints. Chris Whong originally sent a FOIA request to the TLC, getting them to release the data, and has produced a famous visualization, NYC Taxis: A Day in the Life. Mark Litwintschick benchmarked various relational database and big data technologies using this dataset given its moderate 400GB size. And notably, Todd W. Schneider produced some really nice summaries of the dataset, some of which are similar to work I show here. I actually was not aware of Todd’s work on this topic until after this post was written, so although there is a fair bit of overlap, this post and the graphics in it are original. I downloaded the data files from TLC website, and (very painfully) using Python, Dask, and Spark, have produced a cleaned dataset in Parquet format, which I make this available for AWS users at the end of this post. So I was curious, where do taxis pick up passengers, or more precisely, what does the distribution of taxi pickup locations look like? With 1.3 billion taxi pickups, plotting the distribution in a way that does not wash out detail is very challenging. Scatter plots are useless due to overplotting, and 2D histograms are a form of kernel density estimation that necessarily blur or pixelate a lot of the details. Additionally, with the full dataset, the pickup locations alone total 21GB, which is more than the memory of my 16GB laptop. Out of core tools can solve that technical problem easily (and subsampling is easier than that), but what about the visual problem? Human eyes are incapable of absorbing 21GB of information in a plot. The solution to this comes from an interesting library called Datashader. It dynamically generates a 2D Histogram at the resolution of your display (or a specified canvas). Each pixel on the display corresponds to certain histogram boundaries in the data. The library counts the number of data points that fall within those boundaries for each pixel, and this number is used to color the intensity of the pixel. Leveraging Dask, the creation of the histogram can scale to terabytes of data, and be spread across a cluster. Leveraging Bokeh, the final plot can be zoomed and panned. Using techniques from high dynamic range photography, intensity ranges are mapped so that maximum dynamic contrast is present at any zoom level, and in any given viewport. This is what the map of taxi pickup locations (1.3 billion points) looks like over Manhattan, plotted using the Viridis perceptually uniform colormap. The first thing I notice is how clearly I can see the street patterns. In parts of Brooklyn and Queens, the street pattern is sharp. In Manhattan, the pattern is `fuzzier’, especially near the southern tip of Manhattan and in Midtown south of Central Park. There are an awful lot of pickups that, according to GPS coordinates, fall over the Hudson or East rivers, and quite a few pickups that fall in the portion of Central Park where there are no roads. Obviously, not a lot of taxi trips are starting in the rivers surrounding Manhattan, but what this plot shows is instead how important GPS error is. The fuzziness arises from tall buildings which make it quite difficult to get a good GPS fix, and the taller the buildings, the fuzzier the streets look. More broadly, the Midtown area south of Central Park is very bright, indicating a lot of taxi trips start there. The second image is also taxi pickups, but on a much wider scale. Zoomed out, most of Manhattan lights up like a beacon, indicating far more pickups in Manhattan than the surrounding area. But the airports, JFK and La Guardia in particular, also light up, showing nearly as much visual intensity (trips per unit area starting there) as Midtown. Now let’s examine the dropoff locations using the Inferno colormap. At first glance, the dropoff locations look a lot like the pickup locations within Manhattan. The same regions, Midtown south of Central Park, and the southern tip of Manhattan show the brightest (and fuzziest) streets. Zooming out to the broader metro area, the streets in Brooklyn and Queens are much sharper and brighter, indicating there are a lot more dropoffs in the outer boroughs than pickups, and indicating the GPS error in these regions tends to be lower, presumably due to fewer tall buildings. In fact, in some places it looks good enough to use as a street map, indicating a relatively even distribution of taxi dropoffs in Brooklyn and Queens. This is quite distinct from the pickups map, indicating that there are relatively few pickups in the outer boroughs, but a lot of dropoffs there. Many people take taxis from Manhattan to the outer boroughs, but a lot fewer take taxis from the outer boroughs into Manhattan. The last two plots compare pickups and dropoffs on a pixel by pixel basis. Wherever pickups are higher than dropoffs, the pixel is shaded with the Viridis green and yellow colormap. Wherever dropoffs are higher than pickups, the pixel is shaded with the purple and orange Inferno colormap. In Manhattan, the Avenues (North-South streets) are lined with green, indicating more pickups than dropoffs. The cross streets (East-West) are orange, indicating more dropoffs. Practically, if I want to catch a taxi, it is probably easier to walk to the nearest avenue and pick one up there. Zooming out to the broader area, there are a few major streets in Brooklyn and Queens that are green, indicating significant numbers of pickups on those streets, while the other streets remain orange, showing dropoffs from the trips that started in Manhattan dominate. At JFK and La Guardia, the pickup and dropoff areas within the airport are highlighted, with portions shaded in green (pickups), and other portions shaded in orange (dropoffs). Plotting taxi pickup and dropoff locations using Datashader and Bokeh has shown that sometimes GPS coordinate data is quite inaccurate, indicating pickup and dropoff locations in the East or Hudson rivers. We see from the maps of pickups and dropoffs in Manhattan that GPS is strongly affected by tall buildings. Dropoffs in particular show a surprisingly even distribution across the outer boroughs, and every road, and every bridge is highlighted. I find this surprising, as I would not expect many dropoffs to be occurring on the bridges, or in other locations where stopping and letting someone out of the taxi is discouraged, such as the Van Wyck Expressway, which leads to JFK. Yet, such bridges and roads are highlighted, and that makes me wonder if this a quirk of GPS? This is all speculation on my part, but what if GPS devices only update at a fixed interval, such as every two minutes, or whenever it can get a position lock? In that case, a taxi trip would end in a reasonable location, but the data would be recorded as the trip ending somewhere along the route. This would explain how large numbers of pickups and dropoffs occur in seemingly improbable locations. Given the dataset goes back to 2009, and GPS receivers in smartphones have come a very long way since then, I am very curious if it is possible to see improvements in GPS accuracy in the taxi dataset. As a proxy for GPS error, I examined the number of pickup and dropoff locations that are in physically impossible locations, such as in the middle of the Hudson and East rivers. I then plotted the fraction of such impossible trips as a rate of the number of the total trips. Given the uptick in ride-hailing and ride-sharing services like Uber and Lyft, a rate adjustment is necessary. Sure enough, the rate of pickups and dropoffs in impossible locations has fallen by a factor of 4 to 5 since 2009. It is unclear to me what could be causing an annual cycle in 2009–2012, where the error rate increases during summer months. Since 2011, the error rate has been falling substantially, possibly due to a changeover in taxi meters across the taxi fleet, or changes in how the GPS gets reported. The fact that dropoffs are higher than pickups suggest to me that there is probably some support for my theory that GPS devices only update at a fixed interval or whenever they can get a lock on position. It is worth mentioning that this error rate of 0.5% — 0.1% representing is not necessarily representative of actual GPS errors in particular locations. For example, the fuzzy streets in Midtown south of Central Park indicate that position error is much higher there than 0.5%. Also, GPS position can be wrong in a way that does not put it over the water, but over an incorrect land location, which would not be detected by my crude proxy for GPS error. I obtained, cleaned, and plotted the NYC taxi dataset. I produced some interesting visualizations of pickup and dropoff locations that show the majority of pickups and dropoffs occur within Manhattan and the JFK and La Guardia airports, however there are a substantial number of taxi trips from Manhattan to Brooklyn and Queens. Far fewer trips start in the outer boroughs and end in Manhattan. I compared the pickups and and dropoffs on a point by point basis, showing how the avenues in Manhattan have more taxi pickups than the cross streets, which have more dropoffs. I also showed how the GPS locations have questionable accuracy. In Midtown, this is visible by ‘fuzzy’ streets, and a fair number of points that show pickups in impossible locations like the Hudson or East rivers. There are also an awful lot of pickups and dropoffs in locations where it would be inconvenient to drop off a passenger such as the Van Wyck Expressway, suggesting that the clear definition of such streets on the dropoffs map is a quirk of GPS devices updating infrequently. Analyzing the number of pickup and dropoff locations that happen to be in water show a significant 4–5X decrease since 2009, which might be attributable to improvements in GPS technology in taxi meters. Nevertheless, the error in the GPS locations suggest they should be considered with a grain of salt. I will be publishing more data analyses on this dataset over the coming weeks. I make the code available in my NYC-transport github repository. You can view the notebook used to make plots for this post on Github or NBViewer. I have put the original parquet format dataframe containing the taxi data and Uber data (not the subject of this post) on Amazon S3 in a requester pays bucket. If you start an EC2 instance in the US-East zone with a properly configured s3cmd, you can copy the files as follows. Be sure to be in the US-East zone, otherwise you may incur significant bandwidth charges . s3cmd sync --requester-pays s3://transit-project/parquet/all_trips_spark.parquet .The data is approximately 33GB in Snappy compressed, columnar, parquet format. If reading with Dask, using the PyArrow backend is required. This content is cross posted on my blog (mirror).

New ArcGIS API for JavaScript releases are just around the corner! Here is a preview of some of the new capabilities coming in early July.  (Note: These are some of the highlights; a full list of new capabilities and enhancements will be provided in the release notes.) ArcGIS API 4.4 for JavaScript New styles for points in city landscapes: Styling the point data in city scenes can now be done more effectively. Point graphics can be configured to display above buildings with the new relative-to-scene elevation mode. Callout lines can be used to better understand point locations (a callout is essentially extended from the top of the scene).   Highlight in 3D: The ability to highlight features in a 3D scene, with options to configure the color and opacity of the highlight effect.   Styling building data: We added the option to remove building textures to better emphasize thematic mapping of buildings, and also the option to make textures grayscale (one example of when you might want to do this is if you want to draw attention away from the buildings, and highlight a particular set of interest).   Smart Mapping You can now automatically generate renderers for SceneLayers using SmartMapping. Generating type renderers with smart mapping is new to both 2D and 3D views. Note: When we reference smart mapping/generating renderers, we mean that the API creates smart defaults for your map/scene styles on the fly. This capability is typically used in data exploration type apps (as opposed to defining the styling explicitly in code).   PointCloudLayer enhancements Added the ability to add natural lighting conditions to a point cloud layer in order to better distinguish objects.   Better web map support Added support for Map Notes, WMS, and WMTS layers.   OGC support Added support for WMS and WMTS layers.   VectorTileLayer printing This release of the JavaScript API includes a support for vector tile layer printing through client-side image.   Arcade support in popups Arcade expressions can now be applied in the popup’s content. This is useful for situations when you want to display data that isn't present as an attribute value in your FeatureLayer instance. Web maps that have been created in Portal or Online that contain popups with Arcade expressions will be honored in apps built with the JS API, and developers can also write Arcade expressions directly in their code.   Widget standardization In this release, the following widgets have been updated to the widget framework, initially introduced at 4.2: Legend, Popup and Search widgets.   Custom Layers The SDK will include documentation and samples for creating your own custom layers.    ArcGIS API 3.21 for JavaScript  Arcade support in popups As described above.   VectorTileLayer printing As described above.   (...plus minor enhancements and bug fixes)

Wal-Mart is telling Amazon game on. The big-box retailer is reportedly warning some tech companies that if they want Wal-Mart's business, they can't run applications on Amazon's cloud platform, Amazon Web Services (AWS), a handful of tech companies have told The Wall Street Journal. Wal-Mart uses some tech vendors' cloud apps that run on AWS, Wal-Mart spokesman Dan Toporek told the Journal, though he declined to say which apps or how many of them. But Toporek did acknowledge instances where Wal-Mart is pushing for AWS alternatives, the publication reported Wednesday. Representatives from Amazon and Wal-Mart didn't immediately respond to additional requests for comment. Wal-Mart doesn't appear to be alone in this push to leave AWS, either. Other large retailers are reportedly requesting — as Wal-Mart has done — that service providers move away from AWS, technology vendors that work with retailers have told the Journal. Adding to the many growing conflicts of interest, Amazon has confirmed a number of retailers it competes with use AWS, for example GameStop. The battle between Wal-Mart and Amazon is only heating up, after Amazon announced plans last week to acquire brick-and-mortar grocery retailer Whole Foods. With Amazon stepping into Wal-Mart's turf in grocery, Wal-Mart has been trying to beef up its e-commerce presence. Following news last week of an Amazon-Whole Foods buy, Wal-Mart's stock sank more than 7 percent. An Amazon spokeswoman, in a conversation with the Journal, referred to Wal-Mart's latest moves as attempts to "bully" vendors. "Tactics like this are bad for business and customers," the spokeswoman told the publication. Read the complete story from The Wall Street Journal.

The world’s population will break through the 8 billion mark in 2023, there are more men than women, and next year the number of over 60s will top 1 billion for the first time, according to the latest findings and forecasts from the United Nations annual population survey. More than half of the global population growth by 2050 will come from sub-Saharan Africa, where fertility rates will persist at levels far higher than in the rest of the world, the UN predictions released on Wednesday show. Half the growth in numbers of people will come from just nine countries: India, Nigeria, Democratic Republic of the Congo, Pakistan, Ethiopia, Tanzania, the US, Uganda and Indonesia. By 2050 seven of the world’s 20 most populous nations will be African. By contrast, all European countries now languish with fertility rates below replacement level, meaning that populations will inexorably decline without large-scale immigration. “In some countries with low levels of fertility and ageing populations ... a net inflow of migrants has been the primary source of population growth and in some cases has averted a decline in population size,” noted John Wilmoth, director of the UN’s population division. Eastern Europe is likely to be particularly badly affected by population trends, with numbers likely to fall more than 15% in Bulgaria, Croatia, Latvia, Lithuania, Poland, Republic of Moldova, Romania, Serbia and Ukraine. The UN study also found that there are more men than women globally (102 men for every 100 women), and that the number of people over 60 will top 1 billion in 2018 – and 2 billion by 2050. Children under 15 years make up about one quarter of the world’s inhabitants. The median age of the world’s population is 30.

From OSGeo Wiki Status About Details of the Travel Grant Application & Deadline Selection Process Draft edited by Steven on 2017-06-21 Final version, last edited by Till on 2017-06-22 The FOSS4G Travel Grant Program for 2017 is administered by the Conference Committee. This page sets out the process for applying for an OSGeo Travel Grant to attend FOSS4G 2017 and the process and criteria by which applicants will be selected. The OSGeo foundation has made an amount of US $10,000 available to fund a travel grant program The TG covers a Full Conference Pass and an allowance for accommodation and meals/expenses It is anticipated that a TG will not normally exceed $1,000 (the Conference Committee can vary this guideline at their discretion in exceptional circumstances) Workshop attendance is not included in the Conference Pass, reduced rate fees for attendance at workshops will be available to grantees Applicants must be able to fund the direct costs of travel to FOSS4G and any booking deposits for accommodation Applicants must be in possession of a valid entry visa (if required). Successful applicants will receive a conference pass and reimbursement of expenses up to the limit of the Travel Grant when they check-in at the conference registration desk. Applications should be submitted on the [TGP Application Form] by 2017-07-07 CET We expect, that the money for the TGP will be less than the number of people wanting to get sponsorship for FOSS4G attendance The Selection process is partly based on World Bank's Country Ranking that group countries into these groups: LOW-INCOME ECONOMIES ($1,025 OR LESS) LOWER-MIDDLE-INCOME ECONOMIES ($1,026 TO $4,035) UPPER-MIDDLE-INCOME ECONOMIES ($4,036 TO $12,475) HIGH-INCOME ECONOMIES ($12,476 OR MORE) Priority is given to applicants from the first three groups above Additional selection criteria may include Gender Minority status Student Accepted talk Every applicant will be 'blind evaluated' in a secret table, after a calculation between money/costs per applicant travel grants are assigned to the top ranked applicants Applicants will be notified of the result of their application by 2017-07-14 at the latest The decision of the Conference Committee selection group is final and not subject to any appeal

Norway has issued a blunt threat to Brazil that if rising deforestation in the Amazon rainforest is not reversed, its billion-dollar financial assistance will fall to zero. The leaders of the two nations meet in Oslo on Friday. The oil-rich Scandinavian nation has provided $1.1bn to Brazil’s Amazon fund since 2008, tied to reductions in the rate of deforestation in the world’s greatest rainforest. The destruction of forests by timber and farming industries is a major contributor to the carbon emissions that drive climate change and Norway views protecting the Amazon as vital for the whole world. The rate of deforestation in the Amazon fell steadily from 2008 to 2014, an “impressive achievement” which had a “very positive impact” on Brazil and the world, according to Vidar Helgesen, Norway’s environment minister. But in a forthright letter to Brazil’s environment minister, José Sarney Filho, seen by the Guardian, Helgesen said: “In 2015 and 2016 deforestation in the Brazilian Amazon saw a worrying upward trend.” He warned that this had already reduced Norway’s contributions and added: “Even a fairly modest further increase would take this number to zero.” Helgesen said he had serious concern that controversial moves in Brazil to remove protection from large areas of the Amazon and weaken the environmental licensing required for agriculture would worsen deforestation. Furthermore, he said, budgets for the environment ministry and other departments that protect the Amazon had been drastically cut. Brazil’s president, Michel Temer, is seen as close to the powerful agricultural lobby, which is pressing for cuts in Amazon protection. Annual deforestation in the Brazilian Amazon jumped by 29% to 8,000 sq km in 2016, although it remains well below the 19,000 sq km seen in 2005. Norwegian officials say that under the rules Brazil itself set for the Amazon fund, a rise to 8,500 sq km would mean no payments from Norway. Filho, the son of the top landowner in Maranhão state, has replied to Helgesen. “I have made every effort to maintain the course of sustainability with determination and political will,” he wrote. Filho told Helgesen that the latest preliminary data suggested the increase in deforestation rate may have levelled off. “[It] indicates that we may have stagnated the upward curve of deforestation. We hope that the new data will soon point to a downward trend.” Temer is set to face protests in Oslo on Friday from rainforest and indigenous rights campaigners, including Sônia Guajajara, a leader from Brazil’s indigenous movement APIB. She said: “Temer violates his obligations and undermines people’s constitutional rights. His attacks on indigenous peoples and the environment are of a magnitude we have not seen before.” The Amazon fund currently supports dozens of projects which fight deforestation, work on land regulation and the environmental management of indigenous lands. Norway itself was criticised by environmental groups on Thursday, after offering oil companies a record number of exploration blocks – 93 – within the Arctic circle. Terje Søviknes, minister of petroleum and energy, said: “New exploration acreage promotes long-term activity, value creation and profitable employment in the petroleum industry across the country.”

On a clear day, standing on a ridge in the Cascade Siskiyou National Monument overlooking the Soda Mountain Wilderness, I can see the majestic peaks of Mount Ashland and Mount McLoughlin, and even the rim of Crater Lake in Oregon and Mount Shasta in California. And yet the Department of the Interior has listed Cascade Siskiyou National Monument as one of 27 national monuments up for “review” under President Donald Trump’s executive order. Don’t let this fool you: These reviews are the first step toward opening monuments to industrial development, including oil and gas drilling and large-scale logging. I take Trump’s attack personally – Cascade-Siskiyou is in the heart of where I live and embodies the importance of protecting large, wild landscapes. I take Trump’s attack personally – Cascade-Siskiyou is in the heart of where I live and embodies the importance of protecting large, wild landscapes. The monument’s boundaries form a wildlife corridor between the Cascade Mountains and the Siskiyou Wilderness in Oregon and Northern California, where wildlife thrive. On any given day, you might see mountain lions, bobcats, Columbian black-tailed deer, hundreds of species of birds (including endangered species like the gray owl), rare redband trout in Jenny Creek and 135 species of butterflies floating along rolling hills of wildflowers. They couldn’t survive without these protected lands and watersheds. National monuments are set aside by Congress or the president as public spaces because of their historical or scientific significance. This particular monument was created because it’s one of the most bio-diverse temperate areas on the planet. Standing on that ridge, looking north, I think of the famous wolf “OR-7” (nicknamed “Journey” by schoolchildren) who trekked through this land on his incredible travels between Oregon and Northern California. The return of endangered wolves to their native homes is a sign of the region’s health and importance. The handful of wolves in these states, the first in nearly a century, need the protection of this monument. It is a safe passage home. Climbing Pilot Rock, where you can see all the way to the volcano that is Mount Shasta 90 miles to the south, or meandering down the spiny outcrop of Boccard Point, with views of the Cascades and the Siskiyou mountains, it’s clear that this monument is the heart of a region many other creatures call home. It is vital not just to the people who live here but to the incredible ecosystem in its folds. I’ve seen black bears, foxes, eagles and hawks, bats and tree frogs on my hikes along the Pacific Crest Trail, which runs through 19 miles of mixed pines, cedars, Manzanita, junipers and oak groves, and draws tourists from all over the world. Dismantling public lands like this monument could deal a fatal blow to the vital biodiversity of its ecosystems, leaving it looking as tattered and barren as the treeless landscapes of the southern Oregon border of the Siskiyou Crest. And our monument is overwhelmingly supported by local communities, including mayors, city councils, state legislators and local tribes. We want it expanded, not cut. The unique character of the land up here and the diversity of wildlife are rooted in our economy, our identity, our history and our future. No president has ever removed a monument designation and the law doesn’t allow it. But this administration sees public lands as cash cows to be sold, mined, clear cut and grazed ‒ against the will of those of us who live here. Our natural wonders aren’t for sale. We will fight for every acre of the Cascade Siskiyou National Monument. The public can comment on this issue at www.regulations.gov until July 10. Jennifer Molidor leads sustainable food initiatives for the Center for Biological Diversity. She lives in Siskiyou County, just south of the monument’s border, and can be reached at jmolidor@biologicaldiversity.org. Sign up today for a 30 day free trial of unlimited digital access. SUBSCRIBE NOW

Midsummer, heavy heat, and London is beside itself: couples kiss by tube station steps, accordion players linger on street corners, the city is alive with the coatless, bare-legged and bewildered. Across the air comes the sound of last orders, police sirens, blurry conversation, while the backstreets stand quiet, lost in the scent of jasmine and dust. A couple kissing by the steps of a tube station Wednesday brought both the longest day of the year and the hottest June weather in four decades, a combination that seemed to intensify the strangeness of these times - when the hours feel precarious, and every morning brings fresh and unfathomable news of tower block fires, terrorist attacks, votes, revelations. There is the sense that the night is no longer a safe place; it is unsettled, unrested, filled with phantoms. The glow of a TV though a window flung open wide In Kensington, close to midnight, the streets around Grenfell Tower have a leaden stillness. Roads blocked, windows flung open, somewhere an Arabic station playing. Chatting outside a pub in Kensington, graffiti on a street sign, and a poster appealing for information on missing Jessica Urbano Outside a newsagent, a group of young men lounge, sharing a joint. They are listless and open and warm, keen to make conversation – about the fact they have never been further than Fulham, about the trials of trying to get council housing, about the desire for a life that is different. A couple of streets away, amid the makeshift tributes to those lost in the recent fire – the bunches of flowers and cardboard posters tethered to railings – a man quietly draws a piece of paper out of his bag, weights each corner with cans of lager, then sits down on the pavement and begins writing out the lyrics to You’ll Never Walk Alone. Omar attaches the tribute to one of the railings Along the street we find Omar, playing music through his phone, speaking to anyone who will listen. “I haven’t slept properly for five or six days,” he says. “I’ve seen horrific things in my life, but I can’t get this one out of my head. I can hear their screams. I feel shattered, emotionally drained. I can’t go home.” He looks along the road, at the knots of people who cannot sleep, at those quietly walking in the early hours, looking up at the black hulk of the tower. “This is the calm before the storm,” he says, and the night seems to simmer. “You know that don’t you?” London leaves its mark on one of its inhabitants Across the city in Camden the mood is lighter. There are revellers spilling out of late-night bars, sitting drinking on warm pavements, seeking pizza, unsteady on their feet. Smoking outside one of the late bars along Chalk Farm Road Outside the tube, a busker is playing Wonderwall while a crowd sings along, drunk and lusty and unwilling to go home. Three in the morning, 25 degrees, and Hackney stands in the flickering space between late night stragglers and early risers. The kebab shop workers are serving wraps and cartons of chips, counting their way down to 4.30am. On the top deck of a night bus a woman sleeps sprawled across two seats, while another, neatly dressed and ready for the day, shades her face and reads her bible. A man tries to sleep on a bench Central London is coming into life now: the stirring of street-sweepers, shop-shutters, service workers making their way across the city. In the soft warmth of the morning the buildings seem to bloom. Tish, 25, is a volunteer at the Salvation Army From Westminster Bridge, the sky is lifting – wild shifts of peach and blue that light up the river and the rooftops and all the windows of parliament. There is the strange sense of something slipping through: standing in the breeze of the river, somewhere between day and night, watching the morning joggers and the early suits, the lost and the aimless and unslept. Writer Laura waiting for the dawn Then out of nowhere, out of the bright new morning – or perhaps the night now passed – sail two men on a Boris bike: a zig-zagging, half-cut charge towards the south. “Good morning!” cries one as they pass. He is balanced on the handlebars, phone aloft, filming the sunrise. “Good morning!” he shouts again – to us, and the river and the gathering sky, his face lit up, his voice full of laugher, delighted by the new day and all the joys to come.

Drone2Map for ArcGIS version 1.2 is available for download from My Esri and the Drone2Map for ArcGIS Help Site. This release builds on the themes of ease of use and automation by adding in key features for batch processing, sharing web maps, and increasing the quality of 2D and 3D imagery products. Here are some of the highlights of the Drone2Map 1.2 release: Run multiple projects in batch mode Redesigned ribbon for better organization and ease of use Ability to open your Drone2Map projects in ArcGIS Pro directly from Drone2Map Ability to change layer symbology in table of contents Level of Detail (LOD) support for 3D Textured Meshes Additional 3D textured mesh settings for better control of your output products Ability to recreate products without re-running initial processing New draw tools for marking up your Drone2Map projects Ability to share your Drone2Map projects as Web Maps to your sharing portal Improved image viewer with integrated image carousel View the processing log in real time to get more fine grained status Support for setting your output spatial reference without defining GCPs Use custom templates directly from create new project page Ability to select image points from the map when using the image properties window Added support for select calibration parameters Improved settings for default application behavior

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This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Multi-Resolution Land Characteristics 2001 (MRLC 2001) data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. Also highlighted is a subset of an EO-1 Advanced Land Imager (ALI) image. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data.- This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data. This image was created using Tri-Decadal Global Landsat Orthorectified ETM+ Pan-Sharpened data, and draped with National Elevation Dataset (NED) data.

(click for larger version) The decision has been made: 17,410,742 people of the United Kingdom’s 65 million population voted for leaving the European Union. These are about 26.8% of the UK’s resident population, or 37.4% of the electorate in this EU referendum. It also equals 51.9% of the valid votes cast, official figures from the electoral commission. 16,141,241 people (48.1%) voted for remaining a member of the EU, at a turnout of 72.2% of the electorate of 46,500,001. Apart from people not at voting age, EU residents living in the UK were not allowed to vote as other foreign nationals. Irish citizen living in the UK were given a vote in this referendum, as was the UK population in Gibraltar. The following map is a cartogram that shows 327 of the 328 electoral areas (Gibraltar is not shown in this map) from this referendum resized according to their total number of people entitled to vote. In addition, the vote share for leaving and remaining is shown in differently shaded densities. Unlike in the introductory map above (where blue stands for Leave and yellow for Remain), I now switch from the colours that have widely been used in the UK’s domestic media coverage (which even BBC’s David Dimbleby found hard to explain), to a colour scheme that I find more suitable, using blue (from the EU flag) for the remain votes and red (from the Union Jack flag) for the leave votes: (click for larger version) Larger version with additional labels A blog post on the Vis4 website explains why their (nice) mappings of the referendum was not made in cartogram form. I agree with some of the points they make, though I feel that cartograms can provide some useful additional perspectives on the outcome, which is why I have added my cartographic take on the referendum here. No single map can tell the full story of this decision that will have quite a lot of implications for the future of the United Kingdom, the European Union, and quite certainly also the whole world. The next cartogram simplifies the above map by not including information about the vote share, but only showing which electoral area has voted for remaining in or leaving the European Union. The overall patterns show a very divided United Kingdom. The populations of Scotland and Northern Ireland have voted for remaining a member of the EU in all electoral areas, while Wales, and even more so England are split in their verdict of the quite close outcome: (click for larger version) With 72.2% turnout among those that were allowed to vote (approximately 71.5% of the current resident population), participation in this referendum was higher than at last year’s general election (where 66.1% decided to vote), but lower than in Scotland’s independence referendum (where 84.6% of the residents in Scotland voted). The following cartogram shows the geographical variation in turnout: (click for larger version) Cartograms can be used as a basemap for mapping elections and showing them from the perspective of those that matter, such as the electorate used in the above maps. But they can also be used for visualising the actual electoral data. As it was the case with mapping the Scottish referendum in 2014, showing the distribution of Leave and Remain votes very much reflects the distribution of overall votes or the electorate, as it is hard to distinguish the smaller differences between areas that such a close outcome produces. However, with a clearly split country, the dominance of Remain votes in Scotland and London becomes visible when comparing these two cartograms, as shown in the top row of the following map series where I used a consistent colour scheme to highlight the variation of votes between different regions of the United Kingdom (a land area map of the UK is included on the left for reference). The cartograms on the bottom include a cartogram of the electorate as it was used in the above cartograms, and then shows two maps where each electoral area is resized according to the the absolute surplus of votes for either Leave (middle) or Remain (right), so basically are cartograms of the difference in votes in each area for both sides of the referendum. Each electoral area therefore appears exclusively in only one of the two cartograms: (click for larger version) While politicians on both sides of the argument (and outside the UK) scratch their heads about the implications of the referendum, many more maps could be drawn from the results dissecting the demographics that played an important role in the decision. For now, I leave it to these and recommend looking at the New York Times map by Gregor Aisch, Adam Pearce and Karl Russell if you want to see a more conventional perspective made in an impeccable cartographic manner. The content on this page has been created by Benjamin Hennig using data by the UK Electoral Commission. Please contact me for further details on the terms of use. (Visited 1,230 times since December 2015, 1,258 visits today)

GEO Jobe is a Nashville, Tennessee based GIS professional service provider offering UAV data capture and aerial mapping services, data processing and UAV image hosting. GEO Jobe has more than 17 years of experience in GIS consulting, digital mapping, custom application development, enterprise GIS system support and geospatial data acquisition. GEO Jobe has assisted clients from many industries including large utilities, local governments, airports and Universities in the collection, analysis, and dissemination of geospatial data using UAV technology. The GEO Jobe crew currently has three FAA licensed UAV remote pilots experienced in UAV and mobile data collection techniques for orthophotography updating, corridor mapping, asset inventory, 3D building design models and more. GEO Jobe is prepared to support your mapping projects with UAV technologies, ideal for clients in local planning, economic development, utilities, construction, forestry, mining, agriculture and other industries. Project deliverables are provided based on our customer needs, user requirements and can satisfy the requirements of a number of data capture and analysis projects as requested. GEO Jobe can deliver the following: Orthophoto – An orthophoto, orthophotograph or orthoimage is an aerial photograph geometrically corrected (“orthorectified”) such that the scale is uniform: the photo has the same lack of distortion as a map  It is a photographic map. Since an orthophoto has a uniform scale, it is possible to measure directly on it like other maps. An orthophoto may serve as a base map onto which other map information can be overlaid (Source: USGS) Digital Surface Model (DSM) – A DSM is an elevation model that includes the tops of buildings, trees, powerlines, and any other objects. Commonly this is seen as a canopy model and only ‘sees’ ground where there is nothing else overtop of it. DSM is a first-reflective-surface model that contains elevations of natural terrain features in addition to vegetation and cultural features such as buildings and roads. (gistackechange) Digital Terrain Model (DTM) – A DTM is effectively a DEM that has been augmented by elements such as breaklines and observations other than the original data to correct for artifacts produced by using only the original data. This is often done by using photogrammetrically derived linework introduced into a DEM surface. An example is hydro-flattening commonly seen in elevation models done to FEMA specifications. Note, a DEM is a subset of DTM and the most fundamental component of DTM Image Hosting in the GEOpowered Cloud – Hosting the large amounts of data collected from your UAV team doesn’t have to be a huge, expensive headache.  Utilizing GEO Jobe’s GEOPowered Cloud environment we can host your UAV data in many popular cached formats such as an ArcGIS for Server Image Service, MapServer, KML, etc. These services can be registered in products such as ArcGIS Online, ArcGIS Desktop, Google Earth, AutoCAD, as well any third-party applications that can receive an image service. As you perform updates or add more orthos to the service, images will be loaded into the same image service and will automatically appear in any application referencing the image service. 3D products – According to Esri, the world is not flat and you are no longer limited to abstraction. Use 3D to see your data in its true perspective, to make better decisions, and to communicate your ideas more effectively and efficiently.  Data delivered in a 3D suitable format will enable users to “3-D enable” their geographic products. These data will support further viewing and analysis in 3D environments using supported 3D analysis tools and software. Iroquois Steeplechase Grandstand, Warner Park PGUD – Open Ditch Aerial video footage and asset inspection inventories GEO Jobe UAV Services Office, Nashville, Tennessee UAV Services for: forestry mapping agriculture and crop surveys utility corridor mapping asset inventory 3D building design and modeling oil and gas industry asset tracking baseball and sports field asset inventories and vegetation mapping custom aerial/ortho mapping GEO Jobe UAV Resources: UAV Image Gallery UAV Technology tips, links, news See also the GEO Jobe UAV Services Instagram where we frequently share images and video captured in the field on flight assignments and missions. Call for more information about GEO Jobe UAV Flight Planning & Aerial Mapping Services: (615) 883-0085 or Request UAV Service Information using the link below: Save Save Save Save Save Save Save Save Save Save Save Save Save Save

Ad: Share:The European Space Agency (ESA) launched the first of the Sentinel-3 Earth observation satellite constellation in February of 2016.  Two more satellites planned as part of the fleet that makes up Sentinel-3.  Sentinel-3A is carrying a host of sophisticated sensors: SLSTR (Sea and Land Surface Temperature Radiometer), OLCI (Ocean and Land Colour Instrument), SRAL (SAR Altimeter), DORIS, and MWR (Microwave Radiometer).  This satellite will gather a host of earth observation measurements covering the Earth’s oceans, land, ice and atmosphere. A detailed look at all of the data products from Sentinel-3 is available here. If you’re completely new to Sentinel data in general, a good place to start is Andrew Cutts’ overview on what data is available from Sentinel-3 and how to download and use it in his article, “Sentinel 3 for beginners.” The ESA provides a map interface for searching for and downloading from the Sentinel-3 Data Hub.  To find data, either type in a search term or use your mouse to rubberband an area of interest.  A login in required but the interface that pops up provides the guest login information if you aren’t registered.  Once you’ve selected an area of interest, click on the magnifying glass icon next to the search bar to see what products are available for downloading. As Cutts outlines in his Sentinel-3 introduction, the ESA offers a toolbox for viewing and interacting with Sentinel-3 data.  The toolbox is freely available and can be downloaded from the Science Toolbox Exploitation Platform (STEP).  This toolbox provides a set of visualization, analysis and processing tools.  For a detailed look at all of the tools offered, this presentation by Norman Fomferra, Ana Ruescas, Tonio Fincke, and Thomas Storm provides a detailed peek as well as a basic introduction for understanding Sentinel-3 files and loading them into the toolbox. For more in-depth tutorials, the ESA has a tutorial page for using the Sentinel Application Platform (SNAP) toolbox containing a series of video tutorials. Introducing Sentinel-3 – ESA, February 19, 2016 Free and Open Access to Sentinel Satellite Data First Satellite Images from Sentinel-1A First Satellite Images from Sentinel-2 Delivered Advertising

“Publish or perish” has long been the mantra of academics seeking to make a success of their research career. Reputations are built on the ability to communicate something new to the world. Increasingly, however, they are determined by numbers, not by words, as universities are caught in a tangle of management targets composed of academic journal impact factors, university rankings and scores in the government’s research excellence framework. The chase for metricised success has been further exacerbated by the takeover of scholarly publishing by profit-seeking commercial companies, which pose as partners but no longer seem properly in tune with academia. Evidence of the growing divergence between academic and commercial interests is visible in the secrecy around negotiations on subscription and open access charges. It’s also clear from the popularity among academics of the controversial site Sci-Hub, which has made over 60m research articles freely available on the internet. Over-worked researchers could be forgiven for thinking that the time-honoured mantra has morphed to “publish, and perish anyway”. But it was not always this way. A new report I co-wrote with Dr Aileen Fyfe and colleagues, Untangling Academic Publishing: A history of the relationship between commercial interests, academic prestige and the circulation of research, traces the origins of academic publishing to the gentlemen scholars who ran the first learned societies. These institutions often struggled financially and were primarily vehicles for communication. The study then charts the rapid growth that followed the second world war, a phase in which the synergism between the demands of an increasingly professionalised academy and the capabilities of private publishing companies enabled the latter to establish a dominant presence in the marketplace. The report shows how far we have strayed from the core principles of science first identified by Robert Merton in 1942: universalism, disinterestedness, organised scepticism and – most notably and perhaps most surprisingly – communism. The ideals of communism may have faded politically and economically through the 20th century – Merton later preferred the term “communalism” – but the belief in the “essentially cooperative and cumulative quality of scientific achievement” lives on. Those ideals preserve the widespread notion that scholarly information should be shared freely. This is not to say that it should be shared for free – it has never meant that, except at a time when scholarly communication consisted solely of letters exchanged between gentlemen of science – but cost remains an issue. Profit margins well in excess of 30% earned by the likes of Elsevier and Springer Nature stick in the craw, particularly since they depend to a large extent on labour that these large publishing companies don’t pay for. It is curious, at a time when the Conservative and Labour parties are arguing about who can intervene most effectively in the energy market to protect the consumer, that no UK government has ventured to demand value for money from academic publishing, despite its heavy dependence on public financing, since much published research originates in universities and research institutes funded by government and charities. And although digital technology and the internet have created a new terrain in which the ideals of open access have begun to germinate, they have yet to produce a cost-effective and reliable harvest of accessible knowledge. The acquisition of peer review processes that had previously been the preserve of scholarly societies by private publishing companies has combined with the increased dependence of individual academics on where, rather than what, they publish to control the digital revolution in scholarly publishing. This has prevented the full realisation of its promise to make publishing faster and cheaper. But we can take heart from the innovations of new publishers and startups that are, if not yet revolutionising publishing, nevertheless driving a significant phase of evolution. The rise of mega-journals and preprint servers, coupled with moves to enhance data-sharing, are helping researchers to rediscover that sharing information should be the primary role of research publications. They are also helping to address concerns about the reliability and reproducibility of the scientific record that are the misshapen produce of a communications ecosystem in which publishing and prestige have been yoked together too crudely. But we still have some way to travel. History reminds us of our values. It can be muffled by the noise of the day-to-day busyness that obscures our ideals and our greater purpose. By reminding researchers of their fealty to disciplinary communities and of their duties to the public purse, we can clarify the vision of the academic community. The report ends with a recommendation not just for researchers, but for other major stakeholders – principally government, funders, universities and learned societies – to look back at the winding road that has brought us to this unexpected present. It’s time to take back control so that we might yet arrive at the destination we had in mind at the start. Join the higher education network for more comment, analysis and job opportunities, direct to your inbox. Follow us on Twitter @gdnhighered. And if you have an idea for a story, please read our guidelines and email your pitch to us at highereducationnetwork@theguardian.com. Looking for a higher education job? Or perhaps you need to recruit university staff? Take a look at Guardian Jobs, the higher education specialist

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Program details – text for workshops and presentations.

A high school debate tournament highlighted the unconscious Euro-centric bias at the heart of the New Zealand education system, writes Nadine Millar. Here they are. The Hato Pāora College debating team, about to take part in the annual O’Shea Shield a couple of weekends ago. The room is prickly with anticipation. This prestigious speech and drama event involves 17 Catholic schools across the lower North Island, and draws massive crowds. Spectators, my son and I included, jostle for a seat. When the chair of the debate stands to read the moot, a hush falls over the room. He introduces the speakers and opens the floor for the affirmative team to begin. The first speaker from Hato Pāora, a Māori boarding school in Feilding, rises to his feet and clears his throat. Shoulders back, he casts his eye around the room. He acknowledges first the chair, and then the adjudicator. He speaks directly to the opposition, hand extended, and wishes them luck. He thanks the teachers that have helped them prepare for the event, parents and whānau who’ve come to watch, and all their school mates. In a Māori context, opening a speech with a mihimihi, or a round of acknowledgements, is fairly typical. Depending on the occasion, these introductions can be long or brief. It’s a form of respect, with speakers often just as likely to acknowledge the people who aren’t in the room as they the ones sitting right beside them. These unwritten formalities provide a certain structure, but they’re not hard and fast rules. They’re flexible, and can be adapted to suit all sorts of situations. Even, if you like, a high school debate. Unfortunately, the judge of the debate didn’t see it that way. He referred to Hato Pāora’s ‘effusive praise’ as not only unusual, but quite unnecessary. Long introductions waste time, he said, suggesting competitors would do better in future if they just got stuck straight into their arguments. He also reflected on the metaphorical references used to humorous effect by Hato Pāora. Entertaining though it was, the purpose was unclear as none of it really served to advance their argument. Then, looking genuinely perplexed, he said: “I don’t know. Maybe it’s a Māori thing.” It was a throwaway comment, no offence intended, but it’s a reflection of the unconscious bias that lies at the heart of New Zealand’s education system. The kind of bias that most of the time people can’t see because Pākehā culture is hidden inside words like “normal” and “traditional”. Certainly, the judge’s criticisms about long-winded introductions and over-imaginative wordplay is valid in terms of a traditional Pākehā debate. In a traditional Pākehā debate, the roots of which can be traced to Ancient Greece, the primary goal is to win. It’s a contest of logic, style and strategy. Competitive debating undoubtedly develops an invaluable skill set – the very cornerstone of modern democracy. Teams are judged on their ability to present arguments that are logical, consistent, and emotionally appealing. Time is of the essence. Points of information, or interjections, are a way of exposing the weaknesses in your opponent’s argument while they’re in full flight. But what if we take a traditional Māori perspective? The purpose of tautohetohe, or Māori debate, is less about beating the opposition and more about achieving consensus. That’s one reason it can take so long, years even, for important decisions to be made. Pākehā are often critical of this process, because it would be infinitely faster and practical just to put tough decisions to a vote. But in te ao Māori, fairness is not always a function of numbers. You’re unlikely to hear points of information in a formal Māori setting either, because standing up to interject while someone else is holding the floor is generally accepted as a no-no. The real skill in tautohetohe is in listening. No notes, no aids. I’ve seen people stand after several hours of debate, returning to address each speaker point by point. More often than not, a good tautohetohe is entertaining. It has to be, because it can go on for such a long time. That’s why metaphor and word play feature so prominently. Why not have a laugh while you’re trying to convince someone of your point of view? Humour is often just as persuasive as logic, after all. While the historical traditions of Pākehā and Māori debates are equally valid, like so many facets of our education system, only one tradition is ever really acknowledged – the Pākehā one. It’s a bias that is sometimes blatant, other times so subtle you can’t always put your finger on it. One of the Hato Pāora boys described it this way: “It just felt as though the judge was saying we’d done something wrong.” That’s a real shame when you consider that not only were the boys being respectful, they were following tikanga Māori. Is it fair to be criticised for representing your culture? Sure, the boys could have shortened their mihimihi, and played down those metaphors. But the point is, should they have to? Should Māori kids have to stop being Māori, in order to succeed in education? In policy, references are always made to “the gap” between Māori and Pākehā. People talk about “levelling the playing” field, “the long brown tail” and the need to “lift” Māori rates of achievement. It’s an emotionally charged language that continually puts the spotlight on the failure of Māori, rather than the failure of the system. There is never any discussion about what Māori have to give up, sacrifice or leave at the school gate in order to achieve in the classroom. It’s not that Māori can’t compete on Pākehā terms. Of course we can, and we do. But in all this talk of a level playing field, when do we ever propose having a home game? I’d like to see how Pākehā kids fare in a tautohetohe. There’s never any discussion about that, though, because everyone readily accepts that the playing field is a Pākehā one. The turf is not up for debate. The mono-culturalism of our education system is no doubt a key reason Māori kids drop out, or are stood down, at a rate more than double their Pākehā peers. Of those kids that do stay on until Year 13, only three Māori out of 10 school achieve NCEA Level 3 – half the number of Pākehā. There are other options, of course. You can by-pass the inflexible Pākehā system, where things like achievement and success are narrowly defined, and send your kids to kura kaupapa and wharekura. It’s a good choice. The evidence shows Māori kids thrive in a total immersion environment. But it shouldn’t be a case of one system being better than another depending on ethnicity. Parents ought to be able to send their kids wherever they like, and have equal opportunity of success regardless of what system they’re in. Change is slow. When I was growing up in the 80s, racism in the playground was rife. I knew I was Māori, but sometimes I wished I wasn’t. These days, people like to think things have improved – that Māori kids can walk into school each day feeling proud of who they are. But recognition is one thing. We still have a long way to go before we can say our education system truly reflects and upholds Māori values, language, customs and knowledge. It’s a process that begins not with Māori kids, but with the people who influence and shape the system they learn in. From teachers in classrooms, to policy makers in government, to adjudicators in high school debates. It is time that our system truly examined the unconscious bias that sits at its core, privileging one ethnic group over another. It will require people to hold up the ideas and values they have absorbed invisibly, and look at them in a new light. It will mean being open to the ways in which our education system can be improved and enriched by Māori cultural values, as opposed to burdened or threatened by them. It means making room for Māori knowledge at the centre of the discussion, as opposed to on the periphery. It means discarding words like “traditional” and “normal,” as though these words reflect facts we all agree on. Above all, it means praising instead of penalising Māori kids who have the courage to turn up and compete on Pākehā turf every day, all the time holding on to the values that define them. The Society section is sponsored by AUT. As a contemporary university we’re focused on providing exceptional learning experiences, developing impactful research and forging strong industry partnerships. Start your university journey with us today.