iD Editor 2.4.0 : Check out the changes of this new release !!!

iD on browser edit panel

The iD editor is the de facto, browser-based OpenStreetMap editor. It was first launched in May 2013 to ease the editing for the osm contributors. iD is fast and easy to use and allows mapping from various data sources such as satellite and aerial imageries, GPS, Field Papers or Mapillary.

The iD editor is a great way to edit for small and easy changes that don’t require the advanced features of JOSM (a more advanced mapping editor). A detailed tutorial on iD editor is given on LearnOSM iD editor Chapter.

iD editor version 2.4.0 has been released on 25 August 2017 having some remarkable features and bug fixes, such as :
? Release Highlights

? A new global imagery layer: Esri World Imagery.  Esri just made their imagery available for OSM use! (Big thaks to ESRI). Check out the new imagery by opening the Background pane (shortcut B)

? New Features

  • Updates to save workflow :
    • Add review_requested changeset tag and checkbox
    • Add source changeset tag and multiselect field
    • Add hashtags changeset tag, API parameter, and auto fill hashtags from comment
    • Write changeset tags for new mappers to indicate walkthrough progress – These tags all start with ideditor:
    • Write changeset tag for changesets_count – it will contain "0" for someone making their first edit
    • Refactor uiCommit into several smaller modules
  • Add addr:unit input to address field for many countries
  • Make rotation and reflection operations available for more geometry types
  • Change raw tag editor readOnlyTags function to accept array of regular expressions
  • name field is no longer automatically added to every preset
  • Field refactor
    • Add options for fields, allow unwrapped fields (no label, buttons, etc)
    • uiField can now be used anywhere, not just inside the preset editor
    • Rename uiPreset -> uiPresetEditor (consistent with raw tag editor, raw member editor, etc)

✨ Usability

  • In save mode, esc should cancel and return to browse mode
  • Recognize more kinds of concrete surface as “paved”
  • When drawing, ignore accidental clicks on mode buttons
  • Change to 80px arrow key panning (this matches Leaflet default)
  • Smoother border around the round vertex preset icon circles
  • Render railway platform slightly different from sidewalk
  • Treat a few special tags as areas even in the absence of a proper area=yes tag.

? Bugfixes

  • Include imagery offset when calculating tiles for background layer
  • Return to browse mode when zooming out beyond edit limit
  • Make sure bool url params actually contain value ‘true’

? Localization

  • Update Chinese address format
  • Swap placement of increment/decrement spin buttons when RTL
  • Fix RTL styling for info panel close buttons
  • Fix RTL styling for spin control and form buttons

⌛️ Performance

  • Use requestIdleCallback in supported browsers for deferred data fetching
    • Avoid reparsing duplicate entities that appear across adjacent OSM tiles
    • Schedule parsing as a low priority task
    • Schedule redraws during idle browser times

? Presets

  • Add signpost term to guidepost preset
  • Remove maxspeed field from living street
  • Make office=physician non-searchable
  • Add preset for amenity=shower
  • Add preset for emergency=life_ring
  • Allow traffic mirror preset on vertex
  • Add presets for many theme park attractions
  • Improve search terms for wetland preset
  • Add jetty search term to amenity=pier preset
  • Remove bin=yes from excrement bag vending machine
  • Improve search terms for group home and social facility presets
  • Allow aerialway station to be drawn as an area
  • Improve search terms for T-bar lift
  • Add hedge preset to barrier category
  • Add railway presets for Derailer, Milestone, Signal, Switch, Train Wash and icons
  • Add railway preset for Buffer Stop, and icon
  • Replace generic “Reference” field with more specific named fields
  • Add preset for Telecom Manhole

The iD map editor is an open source project. You can submit bug reports, help out, or learn more by visiting their project page on GitHub: :octocat: https://github.com/openstreetmap/iD
Finally big thanks to the @TeamID for all their contribution and continuous effors for making more and more usefriendly iD versions.

Source: http://ideditor.com/ and iD Changelog

Chikungunya Map: Areas at Higher Risk in Dhaka

Chikungunya is a mosquito-borne viral disease first described during an outbreak in southern Tanzania in 1952. It is an RNA virus that belongs to the alphavirus genus of the family Togaviridae. The name “chikungunya” derives from a word in the Kimakonde language, meaning “to become contorted”, and describes the stooped appearance of sufferers with joint pain (arthralgia). Recently it has been much evident in Dhaka city like other areas of Bangladesh.

Institute of Epidemiology, Disease Control and Research or IEDCR gave a list of the locations while presenting a research paper at the Secretariat on Thursday.  The areas are Dhanmondi 32,  Sector 4 and Sector 9 of Uttara, Maddhya Badda, Gulshan 1, Lalmatia, Pallabi, Moghbazar, Malibagh Chowdhury Para, Rampura, Tejgaon, Banani, Noyatola, Kuril, Pirerbag, Rayerbazar, Shyamoli, Monipuripara, Mohammadpur, Mohakhali, Mirpur-1 and Korail slum.

Symptoms

  • Most people infected with chikungunya virus will develop some symptoms.
  • Symptoms usually begin 3–7 days after being bitten by an infected mosquito.
  • The most common symptoms are fever and joint pain.
  • Other symptoms may include headache, muscle pain, joint swelling, or rash.
  • Chikungunya disease does not often result in death, but the symptoms can be severe and disabling.
  • Most patients feel better within a week. In some people, the joint pain may persist for months.
  • People at risk for more severe disease include newborns infected around the time of birth, older adults (≥65 years), and people with medical conditions such as high blood pressure, diabetes, or heart disease.
  • Once a person has been infected, he or she is likely to be protected from future infections.

Diagnosis

  • The symptoms of chikungunya are similar to those of dengue and Zika, diseases spread by the same mosquitoes that transmit chikungunya.
  • See your healthcare provider if you develop the symptoms described above and have visited an area where chikungunya is found.
  • If you have recently traveled, tell your healthcare provider when and where you traveled.
  • Your healthcare provider may order blood tests to look for chikungunya or other similar viruses like dengue and Zika.

Treatment

  • There is no vaccine to prevent or medicine to treat chikungunya virus.
  • Treat the symptoms:
    • Get plenty of rest.
    • Drink fluids to prevent dehydration.
    • Take medicine such as acetaminophen (Tylenol®) or paracetamol to reduce fever and pain.
    • Do not take aspirin and other non-steroidal anti-inflammatory drugs (NSAIDS until dengue can be ruled out to reduce the risk of bleeding).
    • If you are taking medicine for another medical condition, talk to your healthcare provider before taking additional medication.
  • If you have chikungunya, prevent mosquito bites for the first week of your illness.
    • During the first week of infection, chikungunya virus can be found in the blood and passed from an infected person to a mosquito through mosquito bites.
    • An infected mosquito can then spread the virus to other people.

Source: WHO; CDC

Urban Damage Assessment from High Resolution Images using OSM MASK and Neural Network

A complete methodology has been given by Dariia Gordiiuk of Earth Observatory Systems(EOS) Data Analytics for detecting urban damages Applying Neural Network and Local Laplace Filter and OSM masking Methods to Very High Resolution Satellite Imagery. Hope it will help a lot the geospatial analysts.

Since the beginning of the human species, we have been at the whim of Mother Nature. Her awesome power can destroy vast areas and cause chaos for the inhabitants. The use of satellite data to monitor the Earths surface is becoming more and more essential. Of particular importance are the disasters and hurricane monitoring systems that can help people to identify damage in remote areas, measure the consequences of the events, and estimate the overall damage to a given area. From a computing perspective, such an important task needs to be implemented to assist in various situations.

To analyze and estimate the effects of a disaster, we use high-resolution, satellite imagery from an area of interest. This can be obtained from Google Earth. We can also get free OSM vector data that has a detailed ground truth mask of houses. This is the latest vector zip from New York (Figure 1).

Figure 1. NY Buildings Vector Layer

Next, we rasterize (convert from vector to raster) the image using a tool from gdal, called gdal_rasterize. As a result we have acquired a training and testing dataset from Long Island (Figure 2).

Figure 2. Training Data Fragment of CNN

We apply a deep learning framework Caffe for training purposes and the learning model of Convolutional Neural Networks (CNN):

Figure 3. CNN Parameters

The derived neural net enables us to identify the predicted houses from the target area after the event (Figure 4). We can also use data from another similar area which hasn’t been damaged for CNN learning (if we can’t access the data for the desired territory).

..
Figure 4. Predictive Results of CNN Learning

We work with predicates of buildings using vectorization (extracting a contour and then converting lines to polygons) (Figure 5).

..
Figure 5. Predictive Results of Buildings (Based on CNN)

Also, we need to compute the intersection of the obtained predicate vector and the original OSM vector (Figure 6). This task can be accomplished by creating a new filter, dividing the square of the predicate buildings by the original OSM vector. Then, we filter the predictive houses by applying a threshold of 10%. This means that if the area of houses in green (Figure 6) is 10% less than the area in red, the real buildings have been destroyed.

 
Figure 6. Calculating CNN-Obtained Building Number (Green) Among Buildings Before Disaster (Red)

Using the 10%-area threshold we can remove the houses that have been destroyed and get a new map that displays existing buildings (Figure 7). By computing the difference between the pre- and post- disaster masks, we obtain a map of the destroyed buildings (Figure 8).

..
Figure 7. Buildings: Before and After Disaster With CNN Method
 
Figure 8. Destroyed Buildings With CNN

We have to remember that the roofs of the houses are represented as flat structures in 2D-images. This is an important feature that can also be used to filter input images. A local Laplace filter is a great tool for classifying flat and rough surfaces (Figure 9). The first image has to be a 4-channel image with the fourth Alpha-channel that describes no-data-value pixels in the input image. The second image (img1) is the same, a 3-channel RGB image.

 
Figure 9. Local Laplace Window Filter

Applying this tool lets you get the map of the flat surface. Let’s look at the new mask of the buildings which have flat and rough textures (Figure 10) after combining this filter and extracting the vector map.

..
Figure 10. Flat Surface Mask With Laplace Window Filter Followed By Extracted House Mask

A robust library of the OpenCV computer vision has a denoising filter that helps remove noise from the flat buildings masks (Figure 11, 12).

Figure 11. Denoising Filter
..
Figure 12. Resulting Mask. Pre- and Post- Disaster Images After Applying Denoising Filter

Next, we apply filters to extract the contours and convert the lines into the polygons. This enables us to get new building recognition results (Figure 13).

..
Figure 13. Predictive Results of Buildings With Laplace Filter

We compute the area of an intersection vector mask obtained from the filter and a ground truth OSM mask and use a 14% threshold to reduce false positives (Figure 14).

Figure 14. Calculations: Buildings With Laplace Filter (Yellow) Before Damage (Green), Using 14% Threshold

As a result, we can see a very impressive new mask that describes houses that have survived the hurricane (Figure 15) and a vector of the ruined buildings (Figure 16).

Figure 15. Before and After Disaster With Laplace Filter
 
Figure 16. Destroyed Buildings With Laplace Filter

After we have found the ruined houses, we can also pinpoint their location. For this task OpenStreetMap comes in handy. We have installed an OSM plugin in QGis and added an OSM layer to the canvas (Figure 17). Then, we added a layer with the destroyed houses and we can see all their addresses. If we want to get a file with the full addresses of the destroyed buildings we have to:

  1. In QGis use Vector / OpenStreetMap / Download the data and select the images with the desired information.
  2. Then in QGis use Vector / OpenStreetMap / Import a topology from XML and generate a DataBase from the area of interest.
  3. QGis / Vector / Export the topology to Spatialite and select all the required attributes. (Figure 18)
Figure 17. Destroyed Houses Location
Figure 18. Required Attributes Selection To Load Vector Into Ruined Buildings

As a result, we can get a full list, with addresses, of the destroyed buildings (Figure 19).

Figure 19. Address List of Ruined Houses

If we compare these two different approaches to building recognition, we notice that the CNN-based method has 78% accuracy in detecting destroyed houses, whereas the Laplace filter reaches 96.3% accuracy in recognizing destroyed buildings. As for the recognition of existing buildings, the CNN approach has a 93% accuracy, but the second method has a 97.9 % detection accuracy. So, we can conclude that the flat surface recognition approach is more efficient than the CNN-based method.

The demonstrated method can immediately be very useful and let people compute the extent of damage in a disaster area, including the number of houses destroyed and their locations. This would significantly help while estimating the extent of the damage and provide more precise measurements than currently exist.

Source: Dariia Gordiiuk from Earth Observatory System (EOS) 

DATUM for BANGLADESH

A datum defines the reference spheroid (or ellipsoid) used to describe a portion of the earth. In Bangladesh, the Everest spheroid has historically been used in all projections. However, there are various Everest datums and there is a great deal of confusion about which to use. To date it has not been possible to completely clarify the confusion, but an attempt is made here in this regard.

The most commonly use spheroid and the one given in all manuals and textbooks for Bangladesh is the Everest (1830). The British Ministry of Defense, Directorate of Military Survey Plans lists five different Everest spheroids. However, in its table of grids they only refer to the reference spheroid as E”, or Everest, not specifying anyone of the five. Most people have assumed that the Indian IIB grid (used on most of the Bangladesh SOB maps) datum is Everest (1830). Some reliable sources have indicated that the actual datum is Everest (Malaya RKT). The Everest (Malaya RKT) is about 5 m longer on both semi-major and semi-minor axis than the Everest (1830).

In addition there is the confusion about the Indian yard. The Indian yard is defined as 0.914395233 meters, slightly shorter than the international yard (0.9144 exactly). This small change makes a difference when considering the radius of the earth, which is over six million meters.

An Everest spheroid which takes some of these discrepancies into consideration is now being used by Finn map, F AP24 and SOB (but not on all projects, apparently). F AP 24 has called this the Modified Everest Modified. For simplicity this report will refer to it as Everest (Bangladesh).

The difference in semi-major axis lengths between the various Everest spheroids is less the 30 meters. Although there is no linear conversion some tests have indicated that the offset in coordinates is about half the difference of major-axis length in one direction and negligible in the other direction. (example: the difference in co-ordinates between using a spheroid with an axis length of 6,377,307 and one with a length of 6,377,297 is about 4.5 m in one direction and 0.15 m in the other.)

The WGS84 datum has become important in recent years because it is the basic datum for the GP WGS84 provides a best-fit spheroid for the entire earth. It is important to remember that the geographic co-ordinates (latitude and longitude) of any given point are different for each datum. The parameters for the different datums used in Bangladesh are as follow:

Everest 1830

Semi-major axi: 6,377,276.345 m

Semi-minor axi: 6,356,075.413 m

I/f (inverse flattening): 300.801700000

e2 (eccentricity squared): 0.006637847

Source: Most textbooks and manuals

 

Everest (Bangladesh, or Modified Everest Modified)

Semi-major axi: 6,377,298.524 m

Semi-minor axi: 6,356,097.518 m

I/f (inverse flattening): 300.8017

e2 (eccentricity squared): 0.006637847

Source: SOB, F AP24, Finn map

 

Everest (Malaya RKT, or 1948, or Modified)

Semi-major axi: 6,377,304.063 m

Semi-minor axi: 6,356,103.039 m

I/f (inverse flattening): 300.8017

e2 (eccentricity squared): 0.00663784663

Source: British Military survey (may be datum for Indian IIB)

 

WGS84

Semi-major axi: 6,378,137.000 m

Semi-minor axi: 6,356,752.314 m

I/f (inverse flattening): 298.257223563

e2 (eccentricity squared): 0.006694380

Source: Most textbooks and manuals

 

Practically the Everest (1830) is still the most commonly used datum in Bangladesh. However, a number of organizations which have recently investigated the datum and projection problem in detail are using the Everest (Bangladesh). The Everest (Malaya RKT) is not used today, but it appears to be the datum used in preparation of the Indian Zone IIB and IIIB maps.

 

Datum Shifts

Most GPS data is output in the WGS 84 datum, and most of the final output in Bangladesh is in one of the Everest datums. A number of programs require datum shift constants to make the conversion from WGS 84 to the datum being used. However, here too there is some confusion as there are a number of different constants being recommended. The table below gives four sets of constants that are recommended for Bangladesh.

Ref: Water Resources Planning Organization

Universal Transverse Mercator (UTM) Projection System

As the name suggests, the Universal Transverse Mercator projection is based on the cylindrical Transverse Mercator projection. The cylinder in the Transverse Mercator projection is tangent along a meridian (line of longitude) or it is secant, in which case it cuts through the earth at two standard meridians.

In the UTM projection the transverse cylinder rotates by 6° increments, thus creating 60 (360° / 6°) strips or projection zones. In such a projection, instead of projecting the complete globe into a flat surface, each of the 60 strips or zones gets projected onto a plane separately, therefore minimizing scale distortion within each zone. The meridian at the center of each zone is called the central meridian. The cylinder is secant in the UTM projection; it intersects the globe creating two standard meridians that are 180 km to each side of the central meridian. Also since a Transverse Mercator projection results in extreme distortion in polar areas, the UTM zones are limited to 80°S and 84°N latitudes. Polar regions (below 80°S and above 84°N) use the UPS – Universal Polar Stereographic coordinate system based on the Polar Stereographic projection.

The narrow width (6° of longitude) of each zone ensures minimal scale distortion within a zone. Also a map derived from a secant cylinder has less overall distortion than a map from a tangent cylinder. The scale is true (scale factor = 1) on each of the standard meridians meaning that there is no distortion along these lines. Between the secant lines, where the cylinder is inside the globe, features appear smaller than in reality and scale factor is less than 1. The central meridian has a scale factor of 0.9996. At places on map where the cylinder is outside the globe, features appear larger than in reality and scale factor is greater than 1. The scale error within each UTM projection zone remains less than 0.1 percent, or in other words scale distortion is kept to less than 1 part in 1000. Naturally for areas that span more than one UTM zone, the distortion and error increase.

The Mercator and Transverse Mercator projections are conformal projections. In a conformal projection local angles are preserved and shapes are represented accurately and without distortion for small areas. As a result of preserving angles and shapes, area or size of features are distorted in these maps. As mentioned, choosing a secant projection and a narrow zone minimizes the distortions in a map generated from UTM projection.

 

UTM Coordinate System: Grid

By means of a map projection, the earth’s curved surface is transformed to a flat two-dimensional surface. A coordinate system or grid is superimposed on the resulting flat surface. Such a coordinate system provides a referencing frame in order to define the position of objects.

Universal Transverse Mercator is a projected coordinate system, which is a type of plane rectangular coordinate system (also called Cartesian coordinate system). In the two-dimensional surface, two straight lines intersect each other at right angles. These lines are called the axes, and their point of intersection is defined as the origin (0, 0) of the coordinate system. The horizontal axis (east-west) is labeled as x-axis and the vertical axis (north-south) as y-axis. The position of a point in the rectangular coordinate system is defined by its distance from the x and y axes. The two distance values are the x and y coordinates of the point, and use a measurement unit such as meters, feet, etc.

The intersection of the x and y axes in the rectangular coordinate system divides the space into four quadrants. Points along a vertical grid line to the right of y-axis have a positive x coordinate value while those to the left of y-axis have a negative x coordinate value. Points on a horizontal line above the x-axis have positive y coordinate value while those below x-axis are given negative y value. Therefore a point lying in the first quadrant, to the right of y-axis and above x-axis, has positive x and y coordinate values.

In contrast to the projected coordinate system, the geographic coordinate system uses curved grids in order to accommodate the curved surface of the earth; and the geographic latitude, longitude coordinates are measured in degrees, minutes and seconds of arc. These geographic coordinates are converted to plane coordinates by means of map projections.

The UTM coordinate system is a universally used plane coordinate system (except for polar regions). UTM zones are “flattened” using the Transverse Mercator projection, and a rectangular grid network of straight horizontal and vertical lines is superimposed on each zone. Although meridians inside the zone and on zone boundaries converge towards the poles, vertical grid lines are oriented parallel to the central meridian of each zone (and make an angle to the other meridians). Horizontal lines in turn are parallel to the equator. As a result the grid squares stay the same size and shape throughout the map.

The vertical direction of grid lines is referred to as grid north on maps, as opposed to true north which is associated with the direction of meridians or lines of longitude. The angular difference between grid north and true north is referred to as grid declination, and is sometimes indicated on the map margin along with magnetic declination. For large scale maps this difference is small and is usually not taken into account in map reading.

In the UTM grid layout, the unit of measurement is meters, and the coordinates of a point are designated as easting (determine east-west position) and northing (determine north-south position). Vertical grid lines on map are used to find easting while horizontal grid lines are used to find northing of a point; similarly grid lines can be used to help locate a point with known coordinates.

Designation of the central meridian as the reference y-axis (i.e. easting = 0) of the coordinate system within each zone would result in negative easting values for points to the west of the central meridian. For this reason, the central meridian is assigned an arbitrary value of 500000 meters, thus avoiding any negative easting coordinates; points lying to the east of it would have an easting value greater than 500000m and points lying to the west would have a value less than 500000m. This assignment would place the origin outside the zone at 500000m west of the central meridian, as a result the origin is called a false origin and the easting coordinates are referred to as false easting.

The equator is designated as the horizontal reference axis for UTM northing coordinates and is assigned a value of 0 meters North for zones in northern hemisphere. To avoid negative numbers, the equator is assigned a false northing of 10,000,000 meters South for referencing northing coordinates in the southern hemisphere.

UTM Coordinates : Easting & Northing

Easting

The easting coordinate of a point is measured from the false origin 500000 meters to the west of the central meridian of the UTM zone. Within a zone, easting values increase towards east. A point lying 8 meters east of central meridian has an easting of 500000 + 8 = 500008mE. The easting of a point 350m west of central meridian would be 500000 – 350 = 499650mE. The east-west distance between two points is obtained by the difference of their easting values. The distance between the above points is 500008 – 499650 = 358m.

Longitude lines are furthest apart at the equator, where latitude is zero. Therefore the maximum width of a UTM zone occurs on the equator. Depending on the datum and the chosen ellipsoid, an approximate range for the easting values can be calculated. In general the easting values can not be larger than 834000m and smaller than 166000m. As a result, an easting coordinate is always a six digit number. Sometimes in GPS systems and GIS software, the easting values are preceded with a 0 in order to represent them as 7 digit numbers.

Northing

A northing value in northern hemisphere specifies the number of meters a point is located north of the equator. The northing of a point south of the equator is equal to 10,000,000m minus its distance from the equator. In both northern and southern hemispheres, northing values increase from south to north.

A point south of equator with a northing of 7587834mN is 10,000,000 – 7587834 = 2412166m south of the equator. A point located 34m south of the equator has a northing of 9999966mN, while a point 34m north of the equator has a northing of 0000034mN. The north-south distance between two points north of equator with northings of 4867834mN and 4812382mN is 4867834 – 4812382 = 55452m.

Depending on the datum and the chosen ellipsoid, an approximate range for the northing values can be calculated. In the southern hemisphere the northing values range from 10,000,000m at the equator to approximately 1100000m at the 80th south parallel. In the northern hemisphere the northing values stretch from 0m at the equator to around 9350000m at the 84th north parallel. If the northing values are less than 7 digits, they will usually be preceded with 0(s) to represent them as 7 digit numbers.

UTM easting and northing coordinates specify the position of a point on Earth. UTM coordinate of a point is stated by writing the zone, easting and then northing values. When finding a position on a map, it is helpful to “read right up“, that is to read west to east to find the easting and then south to north to find the northing of the location. An example of a complete UTM coordinate: 11U 358657mE 5885532mN.

Since there are coordinate values that occur in both northern and southern hemispheres within a UTM zone, it is important to specify the hemisphere or the latitude band where the point is located. Usually GPS receivers can use both ways to distinguish the hemisphere. Care should be taken so that stating the hemisphere by the use of N (north) or S (south) after the zone number not to be confused with interpretation of N or S as latitude band letters.

Ref: GeoCov

A Brief on Map Projection

Over the centuries, many different ways of representing the round Earth on flat paper have been developed. Each of these methods is referred to as a map projection. What does it mean to project something? Have you ever been to the movie theatre? How does the movie get on the movie screen? The image you watch on the screen is projected using a high powered light from the back of the theater. Now, imagine placing a projector inside of a globe and projecting the different continents, islands, and other features onto a flat screen. What would the projection look like? This depends on where you place the screen. Map projections are attempts to portray the surface of the earth or a portion of the earth on a flat surface. Some distortions of conformality, distance, direction, scale, and area always result from this process. Some projections minimize distortions in some of these properties at the expense of maximizing errors in others. Some projection are attempts to only moderately distort all of these properties.

Conformality : When the scale of a map at any point on the map is the same in any direction, the projection is conformal. Meridians (lines of longitude) and parallels (lines of latitude) intersect at right angles. Shape is preserved locally on conformal maps.

Distance : A map is equidistant when it portrays distances from the center of the projection to any other place on the map.

Direction : A map preserves direction when azimuths (angles from a point on a line to another point) are portrayed correctly in all directions.

Scale : Scale is the relationship between a distance portrayed on a map and the same distance on the Earth.

Area : When a map portrays areas over the entire map so that all mapped areas have the same proportional relationship to the areas on the Earth that they represent, the map is an equal-area map.

The problem is that achieving all of these properties on a two-dimensional map is impossible. Mathematicians have proven this. Preserving any one requires distorting at least one other.

This table shows which properties can be combined in a single map projection:

Equal-Area Equidistant Azimuthal Conformal
Equal-Area no yes no
Equidistant no yes no
Azimuthal yes yes yes
Conformal no no yes

Early maps and the Mercator projection

When people first started drawing maps, they didn’t draw them with mathematical precision, because the necessary mathematics hadn’t been invented yet. The Greeks were the first to do rigorous geometry, and the Greek mathematician Ptolemy (90–168 CE) was the first to develop a map using lines of latitude and longitude and defining locations on a coordinate system. Islamic mapmakers in the Middle Ages built on his ideas, and the Chinese also drew coordinate-based maps to careful scale.

In 1569, the Flemish geographer and cartographer Gerardus Mercator created a map using a mathematical formula to “project” points on the earth’s surface onto a map based on their latitude and longitude. His formula — called the Mercator projection — became the standard means of making maps for navigation, because the directions of the compass corresponded to directions on the map. North, east, south, and west were straight lines on paper, just as they are on the earth’s surface.

Map of the world by Gerardus Mercator, 1569Gerardus Mercator’s 1569 map of the world

he problem with the Mercator projection is that it distorts areas and distances. The North and South Poles are stretched all the way across the top and bottom of the map, and regions to the far north and south appear much larger than they actually are. This isn’t a problem for navigation — Google Maps uses a Mercator projection even today — and the distortion is negligible for maps of small regions (say, of North Carolina). But it can give a false impression of the relative sizes of various countries and continents. For example, on the map below, Greenland is larger than Africa!

World satellite map in Mercator projection              Satellite photography from NASA is used to make a map of the world using the Mercator projection.

Cartographers have developed a number of other projections with various advantages and disadvantages, but the Mercator projection is the vision of the earth that most of us have in our heads more than four centuries later. Different map projections result in different spatial relationships between regions.

Cylindrical Projection

A cylindrical projection map is the most common type of map that we see. Imagine placing the movie screen around the globe in a cylinder shape. The projection that results is depicted in this image. Notice that areas close to the equator have very little distortion. However, the closer to the poles that one travels, the more distorted the map becomes. In this example, Greenland appears to be many times larger than it really is.



Conic Projection

A conic projection map is created by placing a cone shaped screen on a globe. The resulting projection is more accurate than the cylindrical projection map discussed above. However, the further we travel down the map, the more distorted and less accurate the map becomes.

Plane Projection

A plane projection is created by placing an imaginary screen directly above or below a globe. The image that would result is called a plane projection. This type of map projection is not commonly used.

Interrupted Projection

There are many different types of interrupted projection maps. These types of maps try to depict the continents as accurately as possible by leaving blank space in the less important areas of the map, such as in the oceans.

 

Each of the map projections previously discussed as well as any other type of map projection must consider two important factors. What is more important, depicting the accurate sizes of objects on the map, or depicting accurate shapes of these objects? The challenge is that you cannot have both. The more accurately you depict shape, the less accurate will be your depiction of size, and vice versa.

A map which portrays shape accurately is called a conformal map. Conformal maps are useful in that they help us understand the true shape of the items on the map. However, these maps have many drawbacks. A conformal map tends to get quite distorted, especially towards the top and bottom of the map. This creates problems with scale. The scale may be accurate near the equator, but the further one travels form the equator, the less accurate the scale becomes.

Hybrid Maps

Many maps are neither entirely conformal nor entirely equivalent. By blending both conformality and equivalency, we can create a map that balances the distortion of both size and shape.

Thus, some map projections are entirely conformal, while others are entirely equivalent. It is impossible for a map to be both conformal and equivalent. However, many maps are a hybrid between conformal and equivalent.

Properties of common projections

The U.S. Geological Survey provides an overview of several common map projections, with an explanation of how each is created and what it’s most useful for. The table below summarizes the properties of common map projections.  ( Key:  yes yes   partly partly )

Projection Type      Conformal         Equal     area                Equidistant Azimuthal (true direction)
Globe Sphere yes yes yes yes
Mercator Cylindrical yes partly
Transverse Mercator Cylindrical yes
Oblique Mercator Cylindrical yes
Space Oblique Mercator Cylindrical yes
Miller Cylindrical Cylindrical
Robinson Pseudo-cylindrical
Sinusoidal Equal Area Pseudo-cylindrical yes partly
Orthographic Azimuthal partly
Stereographic Azimuthal yes partly
Gnomonic Azimuthal partly
Azimuthal Equidistant Azimuthal partly partly
Lambert Azimuthal Equal Area Azimuthal yes partly
Albers Equal Area Conic Conic yes
Lambert Conformal Conic Conic yes partly
Equidistant Conic Conic partly
Polyconic Conic partly
Bipolar Oblique Conic Conformal Conic yes

Choosing a projection

Finally, this table gives a summary of what kinds of maps each projection is more and less suitable for.  Adapted from the U.S. Geological Survey. Click the name of a projection to get more information about it.

Key:  yes yes   partly partly )

Projection   Type                   World                              Hemisphere                           Continent/
Ocean
                        Region/
sea
                   Medium scale                      Large scale
Globe Sphere yes
Mercator Cylindrical partly yes
Transverse Mercator Cylindrical yes yes yes yes
Oblique Mercator Cylindrical yes yes yes yes
Space Oblique Mercator Cylindrical yes
Miller Cylindrical Cylindrical yes
Robinson Pseudo-
cylindrical
yes
Sinusoidal Equal Area Pseudo-
cylindrical
yes yes
Orthographic Azimuthal partly
Stereographic Azimuthal yes yes yes yes yes
Gnomonic Azimuthal partly
Azimuthal Equidistant Azimuthal partly yes yes yes partly
Lambert Azimuthal Equal Area Azimuthal yes yes yes
Albers Equal Area Conic Conic yes yes yes
Lambert Conformal Conic Conic yes yes yes yes
Equidistant Conic Conic yes yes
Polyconic Conic partly partly
Bipolar Oblique Conic Conformal Conic yes

 

Map projection types all have their pros and cons, but they are incredibly versatile. Even though it is nearly impossible to create an entirely accurate map projection there are uses for even the most imperfect depictions of the Earth. Map projections are created for certain purposes and should be used for those purposes. In the end each and every map projection has a place, and there is no limit to the amount of projections that can be created.

Welcome to the world of modern geography. Enjoy Cartography , Happy Mapping !!

Ref: learnnc; Progonos.com, GISLounge, Kids GeoGraphy

OSM Based Android Apps for Mapping

Though there are a significant numbr of apps are available for tracking, routing, navigation, POI saving but OpenStreetMap based are more useful than others interms of licene, freedom and customization. Below are a short detail of few OSM based mapping app for android

 OSM Tracker

One of the key apps, is something that can track where you are, providing a record of where you’ve been and allowing you to upload it to the OpenStreetMap website. OSM’s heritage is built around collecting data using a GPS, and it still remains a powerfull way of mapping an area without requiring other sources. And we actually are all carrying a GPS device around with us in our pocket it is very easy to do.

I use OSM Tracker it’s a simple app, and does it well, as well as recording a GPS trail, it also allows you to take pictures, and display the track on a map.

Use is really simple, start a new track:

Once it is running, it displays the currently GPS Position, shortcuts for taking a picture, and noting various features:

It is also possible to view the track on a map, this is really useful for checking the current area mapped, and seeing where you’ve been:

Finally, it’s possible to tag save and upload the track:

Vespucci

Editing is generally done as after mapping on a computer, but it can be really helpful to be able to make some changes from your phone, especially once you’re more experienced, I would recommend using Vespucci, Vespucci is getting better and better all the time, and does a really good job editing using the touch interface. This isn’t necessary a beginners tool, but a good tool that continues to improve that’s definitly worth looking at.

When you click on the map, it allows you to choose what item you wish to edit, which is a good solution to the problem of never being able to select accurately enough with your finger. Geometries can then we tweaked as neccessary.

Although I tend to use it to make small changes such as editing tags rather than big edits, it’s a powerfull tool that only gets better.

Data Gathering

Wheelmap for Accessibilty

Wheelmap is a great example of a simple data gathering app. The wheelchair app collects accessibilty information as well as some simple addressing information to be displayed on the Wheelmap Site

 

Keypadmapper for Addressing

As we have mapped a significant proportion of the road network, there is now a big push to collect addressing data Keypadmapper is a simple app to collect addressing data as you move down the road simply enter the numbers as you pass them. Later the address points can be loaded into JOSM for adding into OpenStreetMap.

Find the apps in play store from below link:

Google Map Maker vs. OpenStreetMap: Which mapping service rules them all?

You have a choice when it comes to maps, and the answer isn’t as clear as it used to be. Google’s maps are still king, but OpenStreetMap is making a name for itself, gaining favor among many apps and services that rely heavily on maps, such as Foursquare and Evernote.

OpenStreetMap launched in the UK in July 2004 as an alternative to the large number of proprietary maps that were big in the country at the time. Where does OpenStreetMap get its granular data from? You. Not in an NSA-eye-in-the-sky type of spying, but from information manually input from thousands of casual cartographers. It is truly the Wikipedia of maps. In September 2012, MapBox, developer of the iD mapping editor and one of the main contributors behind OpenStreetMap, received a stipend of $575,000 from Knight News Challenge to further improve OSM’s core infrastructure.

As for Google, it has recognized the usefulness of a ground team – particularly in far flung locations where its Street View contraptions haven’t reached yet. In June 2008, the company introduced Google Map Maker, which allows casual cartographers to add or correct information on Google’s maps. Sounds familiar, doesn’t it?

Though there are similarities between the two community mapping programs, what’s the best one to invest time into if you want to see your mad mapping skillz reflected online?

Open vs. closed data system

osm-screenshot-2The biggest difference between Google Map Maker and OpenStreetMap is how it treats the data you feed it, which may influence your decision on which one to use. OSM describes itself as an open data source, meaning that any person or company is able to use the map information contained in OpenStreetMap. Bear in mind that companies such as Foursquare and Evernote pay MapBox, which creates APIs for OSM, to use the maps for their app, but any information that Foursquare or its users add to it becomes part of and available to all OSM users. In other words, there’s no specialized OSM map that a paying company has access to that a regular Joe doesn’t also have access to.

OpenStreetMap recently switched from a Creative Commons license to an Open Database License (ODbL), which is a share-alike license. It’s similar to the previous Creative Commons license as both allow OSM to be shared and used as long as all of the data one person or company puts into it is made available to all of OSM’s users.

Google Maps and, by extension, Google Map Maker, is a closed system. All of the information you submit becomes property of Google. From the always thrilling Terms of Service page:

By submitting User Submissions to the Service, you give Google a perpetual, irrevocable, worldwide, royalty-free, and non-exclusive license to reproduce, adapt, modify, translate, publish, publicly perform, publicly display, distribute, and create derivative works of the User Submission. You confirm and warrant to Google that you own or have all of the necessary rights or permissions to grant this license. You also grant to end users of Google services the right to access and use, including the right to edit, the User Submissions as permitted under the applicable Google terms of service.

Depending on your personal stance, this may not be a big deal for you. It is, after all, a way to contribute to a map that is pretty much the online standard around the world. Speaking of that, because of Google Maps omnipresence, there’s not always a lot of information to add to heavily populated areas. Much of the major road information missing from Google Maps is in remote parts of the world, such as parts of Africa and Asia.

Speed of updates

As someone just getting started with mapping, you’ll want to see the changes you make as soon as possible, right? Much like Wikipedia, updates made via the Javascript-based iD editor for OpenStreetMap are able to be viewed instantly. However, like Wikipedia, vigilant editors dedicated to keeping the maps correct will remove or alter erroneous edits or additions. So if you label your ex-boyfriend’s house “Dirtbag Manor,” it’s going to be removed between a few hours to a couple of days.

Google Map Maker lets you instantly view your edits, but it cautions that your edit will need to be reviewed before it’s officially added. Oddly enough, even if it’s your first edit to a map, you can still review other people’s edits. In fact, reviewing others’ edits is a way to get your edit reviewed more quickly. However, there’s no telling how long it will take to get reviewed. One edit in our neighborhood had been waiting for review since October 2012.

Interfaces

Google-map-maker-edit-dtGoogle’s Map Maker looks a lot like Google Maps before the most recent update. There’s a column on the left side and the map is on the right. The big difference is that the left column has a header for “My Neighborhoods.” This isn’t the traditional Mr. Rogers definition of neighborhood, but rather geographical locations that you’re interested in. We had a little bit of difficultly adding locations other than where we were currently located, but we were able to add them once we included a city name and state instead of just a zip code. Adding neighborhoods isn’t required to edit a map, but it does provide a general area for viewing and reviewing map edits made by others.

id-ed-area-edit_dtBy comparison, you can view any area on OpenStreetMap with the iD editor and not have to specify geographical areas of interest or expertise.

Adding a road, building, place of interest, or town boundary is similar in both applications. In our experience, the OSM iD editor seemed more user friendly and straight forward. We found it much easier to add a business within a building using the iD editor than it was in Map Maker.

Social component

google-map-maker-welcom-dtIt’s no secret that Google is pushing Google+ extra hard, and Map Maker is no exception. The company encourages Map Maker fans to gather for “MapUps” where amateur cartographers meet up to update Google Maps together. Sounds pretty geeky, right? The MapUp may be held in person or virtually (through Google Hangouts, of course). Google suggests MapUps as a project for a cycling club that wants to add bike paths. The host of an in-person MapUp is elevated in the Map Maker world to an Advocate, as long as at least 20 people attend who each make at least five approved edits.

If that’s not enough cred for you, there’s also a club for Power Mappers. This is for cartographers who make numerous edits and reviews to Map Maker. There’s a private forum and a “unique opportunity to work behind the scenes toward mapping initiatives and product improvements.” Google is really pushing the social side of Map Maker to the point where it seems a little contrived.

Make no mistake, OpenStreetMap is not without its social entities, either. There are numerous mapping meet ups we found listed on openstreetmap.meetup.com and many were taking place this month. We can’t say the same for Google’s MapUps. We only found two events for the month of July, one of which was in Romania. To be fair, Google says it has over 25,000 Map Maker users, while OSM says it has over 1 million.

End of the Road

Ultimately, if you’re interested in cartography, OpenStreetMap is more readily accessible and it’s easier to find others in your locale who share the same interest. Google’s Map Maker is not without its benefits, but our overall experience with it felt more like we were navigating a ghost town instead of a thriving community.

Ref: www.digitaltrends.com