#Parcel Shapefiles

[torontosraccoon]

I’m not entirely sure if this is referring to, for example, open data websites for counties/regions/municipalities/etc. Like, I work on urban planning and let me tell you if a province or a region or county or municipality has shapefiles for data on their open data portals my job is 10000x more accurate and the process of urban planning is facilitated by open data portals. We want to know if there is an environmentally sensitive area, a flood plain, a significant woodlot, the contours of the land, the building footprints, the assessment parcels, and so on and for forth to build better communities. For example, building 15 min neighbourhoods is a big topic of discussion right now and in Ontario some municipalities have moved to incorporate promoting 15 minute cities within their official plans, having things like road centreline data and parcel shapefiles helps me to make a better map and show a municipality why something should be built in a particular area and how it could benefit a community or how it falls in line with their policy documents. I don’t know if the US is doing something aside from this but shapefiles for municipal data and aerial imagery is god actually and I can tell you without a shadow of a doubt that the planners using it do not care about your personal info but rather are using them for their jobs.

One of the most interesting papers I’ve read in years (sci-hub link for those curious) was about the state’s desire to “calculate” its own interior through the use of geospatial technology. It renders all aspects of space inside the borders of a country “calculable”, meaning land is assigned a number that corresponds to some larger system of data. And this effort has its historical roots in settler colonialism, in state surveillance, and a general desire to render everything within a state as knowable as possible - any part of a country may be viewed, at any time, by a government agent, and the corresponding information associated with that land via geospatial coordinates will return information about its size, location, ownership, and other such characteristics deemed important to “calculate”. The eventual goal of this being a comprehensive view of the state as divided into contiguous categorical bins - the location of every citizen knowable and quantified through the logic of numbers. This understanding of the state is modernist and positivist, meaning there is an assumed objective truth about the interior of a state that is discoverable and definable. It views all phenomena as quantifiable and categorical. There is no room for ambiguity in this conception of the state.

And this is reflected in the culture more broadly - up until like the early 2000s, for example, a lot of rural homes in the US didn’t have addresses, just box and route numbers for mail. Like their homes did not have an address, and often the roads they lived on only had informal local names, not official state ones. That seems absurd now with the ubiquity of things like google maps, and that was only 20 years ago! Our understanding of how we access information and move in space is dictated heavily by widespread access to incredibly precise geospatial technology. You can watch your goddamn Uber driver move around on the map on their way to pick you up, and Uber is barely a decade old. There is so little room for ambiguity and uncertainty now.

Anyway this is going to sound like an insane pivot in topic, but I’ve been thinking about the tendency people online have to over-pathologising their behaviour - claiming for example that organizing your notes is a sign of OCD, enjoying puzzles means you have ADHD, etc. And just in general, there seems to be a need to obsessively document every aspect of your personality; peoples’ fixation on personality tests, figuring out what everyone’s “love languages” are, this obsession with finely parsing all the particulars of your sexuality and gender, etc. There is this over-clocking of yourself, trying to calculate and “bin” every part of your identity until there is no room for ambiguity or uncategorised traits. And like idk if this is necessarily connected to what I was talking about above, but it just makes me wonder if, in this age of mass access to information and calculable data, people are trying to calculate the interior of their own personhood as a way to protect against the anxiety that ambiguity/uncertainty often evokes when you run into a piece of information that has not yet been fully calculated.

[clippiner]

Google maps maptiler

#GOOGLE MAPS MAPTILER HOW TO#

#GOOGLE MAPS MAPTILER PDF#

#GOOGLE MAPS MAPTILER FULL#

#GOOGLE MAPS MAPTILER SOFTWARE#

It has been designed for producing seamless maps and aerial photo layers covering whole countries.

#GOOGLE MAPS MAPTILER SOFTWARE#

What is MapTiler It is a software for map tile rendering. The GeoJSON can be converted from / to: ESRI ShapeFile (SHP), DXF, DWG, GPX, CSV and KML. Create rich applications and stunning visualisations of your data, leveraging the comprehensiveness, accuracy, and usability of Google Maps and a modern web platform that scales as you grow. Supported geodata formats by MapTiler: GeoTIFF, TIFF, JPEG, ECW, SID / MrSID, NOAA KAP / BSB, DEM, OziExplorer OZI OZF2 & OZFX3, WebP, JP2, JPEG2000, GeoJP2, Erdas, Grass, Safe, Sentinel2, SRTM, NASA imagery, USGS map sheets, exports from ESRI ArcGIS, ArcSDE, etc. Maps can come from existing tile servers with OGC WMTS, TileJSON or XYZ tiles from. OpenStreetMap vector maps are powered by OpenMapTiles project (the successor of OSM2VectorTiles).

#GOOGLE MAPS MAPTILER PDF#

Import scanned hiking maps, PDF maps, drone aerial imagery, nautical charts for sailing and navigation with a boat or a yacht, fishing maps, aeronautical charts for flight planning by pilots, parcels and city plans and other opendata from local government. Your map can create overlay of standard maps like Google Maps, Yahoo Maps. I it easy to share the data with other people and collaborate! Perfect for mobile data collection for GIS and surveys. MapTiler is described as is graphical application for online map publishing. The app supports GeoJSON and MBTiles formats and data synchronization between multiple devices and desktop computer using cloud. Preview on a mobile device the map you previously designed with own colors and fonts in MapTiler Cloud. Collect field data and notes related to a location, attach a photo and fill custom properties.ĭisplay offline maps generated with MapTiler ().Ĭhoose from various basemaps like street and satellite. The problem is that it generates a folder that contains different splitted image and data. Draw points, polygons and lines on the maps. I already found the software MapTiler which can display an image and the map side by side or one over the other and place some point to possisionate it.

#GOOGLE MAPS MAPTILER HOW TO#

If you want to learn how to initialize a map and load the style see the Learn the basics - How to use OpenLayers tutorial.MapTiler Mobile, डेवलपर Klokan Technologies GmbH से आ रहा है, अतीत में Android सिस्टर्म पर चल रहा है। If you need to develop a web application that interacts with most of the OGC standards, this is your tool, whether connecting to a WFS or reprojecting a raster. It provides an API for building rich web-based geographic applications. Freedom of map hosting Host your maps wherever you want: on your server, in the cloud, behind a firewall or even offline.

#GOOGLE MAPS MAPTILER FULL#

It is a high-performance, feature-packed library for all your mapping needs. pacific coast tiny homes asian hairless pussy tuk tuk edinburgh keansburg website. Features of MapTiler Engine Fast data processing Utilize the full power of your computer to turn the data into maps. OpenLayers is a full featured web mapping library that allows you to display maps from various sources and formats. You can start the map in a different place by modifying the starting position and starting zoom, and you can change the look of the map to any of our styles, or yours, by updating the source URL. Replace YOUR_MAPTILER_API_KEY_HERE with your actual MapTiler API key. Simply use the code below the map and replace the text YOUR_MAPTILER_API_KEY_HERE with your MapTiler API KEY.Ĭopy the following code, paste it into your favorite text editor, and save it as a. This is the easiest and fastest way to use your MapTiler maps in OpenLayers.

[calamp]

The Value of Seamless Esri Map Integration

GPS-based fleet management software gives government, municipal and utility fleets visibility into the location of their assets as well as tools that enable improved operations and fleet performance. Separately, Esri ArcGIS mapping technology lets them create and manage dynamic proprietary map layers.  

When the two work together, the display and analysis possibilities are endless. 

CalAmp iOn™, CalAmp’s cloud-based fleet management software, features a mapping engine that uses the same standards and protocols as Ersi ArcGIS mapping. As a result, it can provide seamless integration with Esri in two directions. 

First, iOn can consume your proprietary GIS map data layers and overlay real-time vehicle location and status data so you can track and manage your fleet in relation to the landmarks, utilities, property parcels, zones, routes and other critical elements of your constantly changing GIS map data. 

​​Any Esri map file in a shapefile format can be overlaid on iOn maps to provide additional, customer specific data in context with fleet information managed by iOn. User data could include anything from shut off valves for a public works department to transformer locations for a utility to warehouse locations for a transportation and logistics company. 

In the other direction, iOn can pump real-time asset location data directly into your Esri ArcGIS application.

In short, exchanging map data is seamless; no API, middleware or third-party plugin is needed. 

No matter whether you’re leveraging the integration in iOn or your own application, it gives public works directors, building inspectors and other operational personnel the power to do more. 

Improving dispatching and fleet management 

Bringing your Esri ArcGIS data into iOn lets you leverage all the functionality of advanced fleet management software against the backdrop of your proprietary maps.

Within iOn, you can easily locate your vehicles on those maps to see which one is closest to a particular service truck or other vehicle, or to a manhole, telephone pole or other infrastructure, to make better decisions about how to allocate mobile workers. Accessing real-time traffic and weather map overlays lets you reroute crews as needed.

Monitoring drivers and vehicle status is simple. Map views show all of your vehicles relative to their routes. Click on a vehicle icon to see who’s driving, whether the vehicle is moving and how fast, and whether the plow or broom is up or down. You can even check the fuel level and view any engine fault codes.

iOn provides historical data as well. For example, you can pull reports to see what percent of time a service vehicle was in motion during its hours of operation and how long it idled. These reports can help you keep contractors accountable and meet emissions goals. 

Breadcrumb trails show an asset’s movement in relation to your own routes and landmarks, as well as the driver’s behavior along the way, including any speeding, harsh braking or fast acceleration.

Creating geofences around your routes, zones, parcels or other landmarks in your GIS lets you track all activity within those geofences. Configure alerts to be notified via phone, email or the notification screen in iOn when a vehicle exits the geofence. When using your GIS map layers in iOn, there’s no need to redraw the geofence when the landmark changes.

Want to see the real-world environment around a parcel, fire hydrant, stop sign or other landmark from your GIS in advance? In iOn, access the Google Maps Street View to glimpse in a pop-up window a recently captured image of the specific area. 

Streamlining fleet operations  

Geofencing the lot at headquarters can automate the check-in of maintenance vehicles before those vehicles hit the road.  

Virginia Department of Transportation (VDOT) fleet supervisors used this approach to check in employee and contractor vehicles when snow plows and graders were called in before a storm. The geofences enabled administrators to not only check in vehicles remotely but also digitally log work hours for more accurate payroll processing. 

Enabling public facing web portals 

iOn can pump automatic vehicle location (AVL) data into applications you build using your proprietary ArcGIS map data. For example, say you want to build a public facing snowplow map that allows residents to see the real-time location of all snow plows in their area. iOn can provide the AVL data to make it happen. 

VDOT launched such a portal in 2018. During or after a snowstorm, residents can type in their address to see whether a plow has been assigned to their area and even track its progress. 

Similar websites can be created to show the location of solid waste trucks, recycling trucks, street sweepers or mosquito sprayers so homeowners know where they are without needing to call your office.

Integrating real-time AVL data from iOn is as simple as clicking “add layer” and plugging in a URL. Voila, your vehicles will show up on the map you’re building.  

Telematics-based fleet management software and Esri ArcGIS mapping tools each bring substantial value on their own. Merging them provides government agencies, municipalities and large companies the ability to better leverage mobile assets, enhance public information services, cut costs and improve productivity in a mission-driven environment.

For more information visit https://www.calamp.com/blog/2021/08/the-value-of-seamless-esri-map-integration/

[tuftsuep]

Kevin Lane

Measuring the Impact of Geocoding Error in a Health Exposure Analysis: a Case Study Utilizing the Connecticut School-based Asthma Surveillance Dataset

Kevin Lane’s thesis explores and explains the potential impact of spatial geocoding error on public health research, in this case an analysis of asthma exposure based on school locations and proximity to roadways. Geocoding is a very typical GIS method used to assign spatial coordinates to phenomena to then use in advanced spatial analysis. This thesis is exceptional in its use of a large surveillance public health data set; its use of sophisticated geospatial and quantitative analysis to estimate the amount of error inherent in the use of geocoding, and to estimate the potential impact of that error on research results. The research is highly valuable to public health and other researchers who want to understand spatial error at the neighborhood scale.

Abstract

The use of Geographic Information Systems (GIS) as a tool to analyze individual exposure to traffic-related air pollution has started to increase in the field of epidemiology, with a primary focus on how to properly classify exposure groupings, and what is the best marker to characterize exposure. Other studies have investigated the affect geocoding can have on spatial accuracy with some attention being paid to analyzing the amount of positional error when using different geocoding address databases. 

To a lesser extent researchers have started to explain the need to establish a “gold standard” for locating addresses, such as using GPS, aerial orthophotography and in some cases parcel shapefiles. Most geocoding positional error studies that have used a gold standard to verify a geocoded location have used small cohorts and only reported on the amount of error, or missassigned exposure points. This study adds to a growing body of research on determining the amount of positional error when geocoding by conducting a proximity to major roadway exposure analysis, where Connecticut (CT) school locations are geocoded using two address databases (Census TIGERline & StreetMap USA) and comparing them against a True Ground Location (TGL) established using aerial orthophotography. This study will then expand upon this research by analyzing the affect positional error can have on results of a health study by comparing the ORs of the schools assigned to exposure groupings using TIGER to geocode their location versus the TGL.

[theresawelchy]

Deep learning in Satellite imagery

In this article, I hope to inspire you to start exploring satellite imagery datasets. Recently, this technology has gained huge momentum, and we are finding that new possibilities arise when we use satellite image analysis. Satellite data changes the game because it allows us to gather new information that is not readily available to businesses.

Why are satellite images a unique data source? What is currently available, and what properties do you have to take into account when choosing which images to use?

Satellite images allow you to view Earth from a broader perspective. You can point to any location on Earth and get the latest satellite images of that area. Also, this information is easy to access. There are free sources that allow you to download the mapped image onto your computer, and then, you can play with it locally.

One of the most important aspects of using satellite images is that you can also browse past images of certain locations. This means that you can track how the area changed over time and predict how it will change in the future. All you have to do is define the properties that are relevant to your use case.

To give you an idea of how satellites track our progress on Earth, we have to take a look at what is above us.

Source: European Space Agency

There are currently over 45 hundred satellites orbiting the Earth. Some are used for communication or GPS, but over 600 of them are regularly taking pictures of the Earth’s surface. Currently (as of end of 2018), the best available resolution is 25cm per pixel, which means that 1 pixel covers a square of 25cm x 25cm. This translates to a person taking about 3 pixels on an image.

The current technology we have actually allows us to get an even better resolution, but it is not available, as many governments don’t allow us to take more detailed images due to security reasons. Meaning, you won’t be able to access better quality unless you have security clearance.

Available sources of satellite images

The first group is free public images. Amongst them are American Landsat and European Sentinel, which are the most popular free images. Landsat will provide you images with a resolution of 30m per pixel every 14 days for any location. Sentinel will provide images with a resolution of 10m per pixel every 7 days.

There are also commercial providers, like DigitalGlobe, that can provide you with images with a resolution up to 25cm per pixel where images are available twice a day. It is important to strike a balance between the different properties that you need, as the best resolution doesn’t always mean that you get the most frequent images.

Also, cost is an important factor. The best images can cost up to a couple hundred dollars so it is wise to start building your solution with lower quality images. Just make sure you use the best ones for your particular use cases. Of course, commercial sources offer subscriptions, which will reduce the images’ cost.

Properties of satellite images

Let’s go through the properties that you have to balance out when choosing an image source. First is spatial resolution. As you can see, technology has been rapidly advancing, and there is more and more money being invested into launching better satellites and making them available.

The second factor is temporal resolution. This is how often you get a picture of a given place. This is an important aspect because of how clouds may block your point of interest. For example, if you only get 1 image every 7 days, and your location is in a cloudy area, then it is likely all your images in a month might be blocked by clouds, which stops you from collecting data in your area. There are some algorithms being created to mitigate this issue, however, it is still a big problem when browsing images. For the most part, it is better to get the highest possible frequency to improve your chances of getting a clean shot of the given area in the selected time frame.

Now, the third factor is interesting. It is spectral resolution. When you think about an image, you usually think of three layers: red, green, and blue; these layers compose a visual image of the area. This is because our human eye has three color-sensitive cones, which react to red, green, and blue.

Satellites offer more than RGB photos

However, satellites can have many more sensors that allow them to record spectrums that our human eyes cannot see. An image taken by the satellite can have 12 or more layers, and each layer brings more information. By combining the layers, you can create indicators that will give you additional insight about what is happening on the ground.

One fascinating indicator is the normalized difference vegetation index (NDVI), which can be used to estimate the condition of the plants. When you look at a normal picture of a field, you see different shades of green, but it doesn’t tell you how healthy the vegetation is.

We can measure vegetation health by looking at the near-infrared light that gets reflected from flora in different ways depending on the amount of chlorophyll. This allows us to see how healthy the plants in our observation area are, which is not possible to derive from an RGB image.

Another example is soil moisture, meaning how wet the land is. During droughts, like in LA, authorities introduced water restrictions. It turned out that wealthier individuals didn’t follow these restrictions and continued to use large amounts of water. Thanks to satellite images, the government could see which fields had high soil moisture, helping them to better enforce these water restriction laws.

It is, also, worth mentioning that there is radar technology that allows you to see through the clouds, but it won’t fit every use case you may want to apply it to.

The current, state-of-the-art satellites have 25cm resolution or images twice a day. This is an example of a standard image.

Sydney Beach by DigitalGlobe

Clearly, you can see people, and you can even count the number of tables outside the restaurant.

As I said before, you have to strike a good balance between these properties to serve your problem. Spatial resolution may not be the most important factor in your research. You also need to consider the temporal and spectral resolutions, cost, availability, and ease of processing.

How can we leverage this data source in our R projects?

Let’s start with what shouldn’t be done in R. There are two main categories: data pre-processing and resource intensive operations. One image will weigh around 1GB and will cover a large area, like half of the state of Washington.

Downloading 100 images and cutting them on your computer is very resource intensive and shouldn’t be done locally in R. There are platforms available that will do the pre-processing and send you the small cut outs of the shapefile that you want. Amongst them, there is Google Earth Engine and Amazon Web Services (AWS), which allow you to simply query the API. They already have public image sets available, and you can upload your own image sets. All of this is available at your fingertips. You just say, “Google, I want a set of dates for Sentinel images that cover small square containing Loews Hotel,” and you are set. From there, you choose one or more dates and ask the API to send you already cropped images, reducing the image size by hundreds of kilobytes.

This all happens quite quickly, as you’re using huge distributed infrastructure to do the calculations. In addition, you can actually conduct computations there and receive indicators. For example, you can receive the NDVI indicator, which is a simple, mathematical combination of the near-infrared and red channels.

R Shiny dashboards for satellite imaginary

Now, R shines when you build dashboards to present the data. You can analyze and forecast the indicators that you’ve built. Operating on small images allows you to leverage many useful R packages to experiment with this data and gain valuable insight. Of course, you can also build neural networks that will help you indicate objects on these images.

Here is an example of a dashboard that you could build with R.

By combining publicly available geospatial data for parcel shapefiles, you can draw any parcel on a map and request available dates of images for that parcel. Then, you can analyze the image, indicate where crops are destroyed, or where they are unhealthy.

This is an example of visualizing an NDVI indicator.

As seen above, there are sub-areas with healthy crops, while there are others with unhealthy plants. Also, the clouds here are distorting the results, which should be accounted for.

One example of applying deep learning to the pre-processed images that I can share is one where we used Kaggle data to indicate if there was a ship located in an image. If you don’t have such a data set available, you have to combine other data sources.

We can use a system called AIS, it requires ships to report their positions on a regular basis. From there, we got a satellite image of the sea, combined that with the ship positions at that time, and cut out the images to prepare the data set.

In the maritime industry, it is important to know where ships are, as there are some restricted areas. For example, there are areas where it is forbidden for fisherman to catch fish. Some of them would turn their AIS off and go there to catch fish. Scanning the satellite images allows us to identify some of these illegal acts.

Also, R is great to augment your data set and produce even more examples.

Here is the network that we used to identify these ships.

It consists of two convolution layers, one max pooling, two convolution layers, max pooling again, and then a softmax function. We also use a dropout to avoid overfitting. Using Keras simplifies the process of defining a network like this.

In this problem, we achieved 98% accuracy, and if you’re interested in the details, you can check out this article on our blog by Michał Maj.

The architecture of complete satellite imagery solution

Now, let’s break down a full architecture of a solution that you could build to analyze satellite images and present the results.

First of all, you need a data source. It can be either a platform or you can get them from providers.

You have to pre-process them, and you need large resources here so it is useful to either build your own solution in cloud or leverage existing platforms. You can batch process many images at once and store them. You can also use an API to get the pre-processed images on demand.

With the prepared images, you can train your network and save the model. Then, you can run a batch process to label your images and store them. Finally, you can build a dashboard that will use them or use the API to request an image, run the model on it, and present results.

Although presented architecture is based on R & Shiny, Python is suited for this job as well and we tested it in our commercial projects.

Business applications of satellite imagery

Let’s look at some emerging applications in different business areas. These are just some examples, but we are seeing more and more different use cases each day.

First is agriculture. Farmers can have a live overview of their crops that show their crops’ health and damages. Using this technology, they can quickly estimate their losses after drought, flood or hurricane. Recently is also used in fertilizing process.

Second is real estate. For example, construction companies can use this technology to see what their competition is doing and benchmark their performance comparatively. Further, they can see which areas are expanding to know what locations might be good to invest in. Also, rooftops themselves provide information about the state of a given building, which is valuable to builders.

The third is finance and insurance. Traders can forecast the supply of goods based on the number of containers being delivered from particular areas. This gives them a huge advantage. For example, they can predict that the price of the given goods will rise in the future if there is a supply shortage. With better spatial and temporal resolutions, you can learn more details about an area. For example, you can use this technology to identify cars in different neighborhoods and assess if an area is wealthy. There were even use cases where the companies count the number of cars in a parking lot to see how well a given supermarket was doing. Note that for this type of work, you need to use much more frequent data.

Summary

The technology is getting better and better. If you start experimenting with these images now, you will be on top of this wave soon. It is important to realize that these techniques are technology agnostic, meaning they don’t only apply to satellite pictures.

In the future, it might be possible to even apply these techniques to live drones or airships. The industry is on the rise, and if you start soon, you can get big returns in the future. Use images, share your results with the community, and, most importantly, have fun. Playing with these images, while valuable, is also very exciting.

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DataTau published first on DataTau

[mrrolandtfranco]

What’s New in Web AppBuilder for ArcGIS (June 2017)

Checkout our new product logo? Pretty snazzy eh? The new icon reflects the idea of a moving gear, the 3 shapes that surround the central white hexagon gives a sense of rotational movement and their protruding ends appear to extend a bit from the central shape like gear teeth. We feel this promotes the concept of a spinning ‘widget’ that powers your web apps built on Web AppBuilder for ArcGIS.

Pop culture side note: The logo’s gear teeth when connected to other gears creates motion, which, when arranged in a certain way resembles a “fidget spinner”. FYI “fidget spinners” are trending and very popular right now (those of you with kids will know what we’re talking about).

Let’s checkout what’s new in this “summer 2017” update.

8 new widgets This release includes 8 new core widgets, which provides some great new functionality in Web AppBuilder. Several were contributed by the Esri Solutions Team – so you may already be familiar with some of these capabilities.

Coordinate Conversion widget– This widget enables you to input coordinates using one coordinate system and output to different coordinate systems using multiple notation formats. These include: - Degree-based formats (DDM, DMS, and DD) - Global Area Reference System (GARS) - Military Grid Reference System (MGRS) - United States National Grid (USNG) - Universal Transverse Mercator (UTM) - World Geographic Reference System (GEOREF)

Simply click a location on the map and its spatial coordinates will display in the coordinate systems selected in the widget.

Full screen widget – This enables web apps to launch in full screen mode in your web browser.

Grid Overlay widget – This will render and display a US Military Grid Reference System (MGRS) grid dynamically and at different index levels inside the application based on the scale of the map display. FYI, MGRS is an alpha-numeric system, based upon the Universal Transverse Mercator (UTM) and Universal Polar Stereographic map projections, for identifying positions. You can configure properties of the grid appearance such as line color, spacing, and label font size at each unique index scale.

Infographic widget– This widget includes 8 graphic templates to visualize and monitor attributes and statistical data. Think of this as an enhanced charting widget for data visualization. You can use a graphic template to visualize field values, field statistics, or feature counts. The 8 graphic templates are: number, gauge, vertical gauge, horizontal gauge, pie chart, column chart, bar chart and line chart. The widget’s visualization graph is dynamic and refreshes whenever the map extent or data source changes, and is interactive with the map.The widget supports two data sources: feature layers in the map with query capabilities and additional data sources (e.g., an output layer from another widget, such as the Query widget or Geoprocessing widget; or a data source specified on the Attributes tab in the Web AppBuilder builder environment).

Parcel Drafter widget – This widget is meant for precision parcel editing by entering metes and bounds descriptions and checking for closure errors. It can be used by mapping technicians in Assessor Offices and Register of Deeds in local governments to verify deeds and land record documents. It can also be used by surveyors and title companies to verify survey information prior to submitting their documents to those offices. Learn more about this widget here.

Screening widget– Enables you to define an area of interest (based on a placename and buffer distance; drawing a point, line, or polygon; an input shapefile that defines the spatial extent; or a coordinate location and buffer distance) and analyze specified layers for potential impacts. For example, the environmental impact of a proposed new development project. After defining the area of interest, the widget will analyze its effect on the specified layers, based on the amount of overlap. It reports results of the analysis by summarizing a count of intersecting features and length or area of overlap. The analysis results can be shared with others as a printed report, CSV file, and file geodatabase or shapefile download. Learn more about this widget here.

Suitability Modeler– This widget helps you find the best location for an activity, predict susceptibility to risk, or identify where something is likely to occur. It allows you to combine and weight different input layers so you can evaluate multiple factors at once. For example, you can use this widget to determine the optimal locations for a new commercial development property.This widget uses fast, web-based Weighted Raster Overlay (WRO) to generate models from a service. You can start from a blank state of a WRO service or a pre-configured WRO model. (FYI: Esri provides several Weighted Overlay Services in ArcGIS Online that are publicly available. These include: World Ecophysiographic, USA Landscape, and Green Infrastructure Suitability  - all of these services have global coverage.) Choose layers, assign weights and adjust layer classification values to define your analysis. Then run the modeler, visualize results, and optionally save the result as an item in your ArcGIS organization. Learn more about this widget here.

One new widget was added for 3D web apps,

Basemap Gallery (3D) widget – This widget displays a collection images representing basemaps from your organization or a user-defined set of map or image services.

New Dashboard theme The new theme displays all the widgets in the panel simultaneously when the app starts. It is designed to visualize widgets and their communication directly. You can modify the predefined layout by adding, removing, or resizing the grids in the panel. By default, most on-screen widgets are turned off except for the Home and Zoom Slider widgets. Optionally, you can turn on the Header widget to display the logo, the app name, and links.

Widget Enhancements

The Basemap Gallery widget in 2D and 3D apps now supports vector tile basemaps.

The Group Filter widget has a “Persist after widget is closed” option so its filter still applies in the app after the widget is closed.

The Edit and Smart Editor widgets now have support for organization members to edit public feature services regardless of their edit privileges.

The Info Summary widget supports showing all features rather than filtering by extent, alphabetizing list content, expanding the first layer in the widget when it is first accessed, and improved handling for filtered layers. The widget panel will also now be sized to fit the list content.

The Situation Awareness widget supports sharing analysis results via printed report, sharing snapshots into a selected group, and has improved handing for layer visibility and services using subtypes.

The Smart Editor widget now supports automatically saving edits so you can quickly digitize new features. It also enables geometry edits by default so you can quickly modify the shape of a feature.

The Time Slider widget has an improved user interface and user experience.

General Enhancements

In the Builder, the Attribute tab has a new option to reference additional data sources that can be shared at the app level, so all widgets in the app can quickly access and respond to it simultaneously.

In the Builder, the Map tab has an option to set the refresh interval in sync with the latest data.

Web AppBuilder now partially supports the Shared Theme that is defined in your ArcGIS organization. Supported items include: logo, logo link, and header color for text and background.

You can create 3D apps from the Share dialog in the Scene Viewer.

We hope you enjoy these new enhancements to Web AppBuilder and see you at the Esri UC!!

Sincerely, The Web AppBuilder for ArcGIS Dev team

from ArcGIS Blog http://ift.tt/2s3FYHM