Urban Tag Tutorial

Start website: 216.47.146.51/dmp/tagger

Input your username and password (default password is 123456). Note to disginguish capital and Incapital letter.

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Clic on “tagaway” to start.

How to tag:

1, Left click on the place you want to tag

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2, Left click on the tag which you just added, then you can select what activity you do or you saw others do there.

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3, If the activity you want to choose is not available in the drop down list, you can type it into the blank rectangle after activity drop down.

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4, Then you can choose your attitude towards that activity.

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5, When you finish all this, just click on “Save Tag to DB”

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6, Then you can start to add new tag.

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Tag the place

We did a exercise about letting people tag on place in main campus, a panorama that simulate augmented reality app that people can actually tag on. This exercise help us create the first generation data base.

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Metaio developer portal: Location-based AR

Introduction

In this example we will use non-optical tracking technology in our scene. Getting location information from the device itself, we will load two POI (points of interest) or billboards North and South from our current position. Then we will add  3D model in the West direction from us. By exploring the scene user has to turn the device around it to discover the objects.

In this tutorial we also introduce LLA coordinates of a virtual object. LLA stands for latitude, longitude and altitude and is a format for defining geo positions on the globe. While the altitude is measured in meters above sea-level, the latitude and the longitude are measured in degrees.
Also integrated into this tutorial is a new feature called radar introduced in metaioSDK 4.1 release. It is quite similar to the radar object presented in previous release of Junaio. In a nutshell, the radar object shows the number and also the location of geometries added to the radar according to the field of view. It provides useful information to users in a location-based tracking application.

Assets

Few things to understand on the resources side. In Tutorial5/Assets5 in iOS and assets/Asstes5in Android you can find

  • POI_bg.png is a background image for billboards we will be using.
  • radar.png is a background image for the radar object.
  • yellow.png is a small yellow dot that represents a geometry in the radar.
  • red.png is a small red dot which represents a geometry in the radar that has been touched.

If you are planning to use billboards in AREL in location-based scenes, make sure that you have downloaded billboard image sets for iOS and Android. The former one has to be simply added to your Xcode project and latter one has to be placed in res/drawable-nodpi folder in the Android project tree.

Implementation

Before loading content we first set the tracking configuration of the scene using keyword GPS, as in the code snippet below.

The Rise of Augmented Reality

The rise of an intelligent social web is creating a disruptive new force in business as public appetite for immersive computing grows.

This demand for richer, more engaging computing is a compelling manifestation of Augmented Reality (AR) and Artificial Intelligence (AI) – two fields of innovation now bursting into the public spotlight.They represent a phenomenon where the real word and virtual worlds are blended on handheld devices and wearable computers.

The release of Google Glass today is a popular case in point. Google’s glasses are a form of wearable computing that locates a mini-projector in the wearer’s peripheral vision. This tiny screen overlays the real world with contextually relevant information from the web.

People wearing Google Glass can see real-time data from the web including the names of objects, mapping directions, contact information and recent social media activity. Google Glass will also give people the ability to record high-definition video, run apps, and respond to voice commands.

However, Google Glass is just the tip of the iceberg.

Wider fields of user-centric innovation are being forged across a variety of industries including health, telecommunications, property, entertainment, education, marketing, gaming, and personal training.

Well-known examples include Apple’s Siri voice recognition technology and rumours of an iWatch, Nintendo’s Wii controller, and Microsoft’s Kinect object recognition technology.

According to Dr. Ashwin Ram, Innovation Fellow at Xerox PARC and a speaker at Amplify Festival, we’re witnessing the rise of an “intelligent social web.”

Human cognition and our social interactions are set to be transformed by artificial intelligence technologies and user-centric devices such as wearable computers.

“These technologies combine the benefits of the ‘information web’ with those of the ‘social web’, enabling new consumer-centric approaches to health and wellness that increase engagement, improve health literacy and promote behavior change,” he writes on his blog Cognitive Computing.

– See more at: http://www.amplifyfestival.com.au/news/rise-augmented-reality#sthash.UpopNwsB.dpuf

Yelp Monocle

Social reviewing service Yelp provided the iPhone with its first augmented reality app, the Yelp Monocle. If you’re in a strange city and you’re looking for good eats, Monocle is your best friend. It’ll use the phone’s GPS and compass to display AR markers for nearby restaurants, bars, and other businesses in real time. Given how Yelp’s high level of success as a user-generated restaurant review service, Monocle is hands down the best app for finding a quick bite to eat.

Read more: http://www.digitaltrends.com/mobile/best-augmented-reality-apps/#ixzz2fPgmLbCQ
Follow us: @digitaltrends on Twitter | digitaltrendsftw on Facebook

Crime In Chicago

1,Crime in your Community

The table below shows how many crimes per thousand people were reported in each of Chicago’s 77 community areas. (What’s the difference between a neighborhood and a community area?) To the right of each per-capita number is that community area’s rank. Community areas often tie for any given rank.

The Chicago Police Department uses more than 30 different classifications for crime. For ease of understanding, these pages focus on three major categories of crime in Chicago: violent crime (such as homicide), property crime (such as theft) and quality-of-life crime (such as prostitution). More about these categories »

Click the image below will link to the main website.

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2,Chicago, IL Crime Information and Alerts

From this website you can tracked the resent crime in chicago area. You can also find a specific kind of crime and see where and when it happened.

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http://spotcrime.com/il/chicago

County-to-County Migration Flows

Tables: 2006-2010 5-year ACS  http://www.census.gov/hhes/migration/data/acs/county_to_county_mig_2006_to_2010.html

Table Notes:
Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.

 

  • Documentation 
    • 2006-2010 American Community Survey County-to-County Migration Files [DOC – 214K] [PDF – 418K]
  • County-to-County Migration Flows 
  • County-to-County Migration Flows by Sex 
  • County-to-County Migration Flows by Age 
  • County-to-County Migration Flows by Race 
  • County-to-County Migration Flows by Hispanic or Latino Origin 
  • County/MCD-to-County/MCD Migration Flows  
  • County/MCD-to-County/MCD Migration Flows by Sex 
  • County/MCD-to-County/MCD Migration Flows by Age 
  • County/MCD-to-County/MCD Migration Flows by Race 
  • County/MCD-to-County/MCD Migration Flows by Hispanic or Latino Origin 

Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.

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