Neighborhood boundaries based on social media activity

Researchers at the School of Computer Science at Carnegie Mellon University investigate the structure of cities in Livehoods, using foursquare check-ins.

The hypothesis underlying our work is that the character of an urban area is defined not just by the the types of places found there, but also by the people who make the area part of their daily routine. To explore this hypothesis, given data from over 18 million foursquare check-ins, we introduce a model that groups nearby venues into areas based on patterns in the set of people who check-in to them. By examining patterns in these check-ins, we can learn about the different areas that comprise the city, allowing us to study the social dynamics, structure, and character of cities on a large scale.

It’s most interesting when you click on location dots. A Livehood is highlighted and a panel on the top right tells you what the neighborhood is like, related neighborhoods, and provides stats like hourly and daily pulse and a breakdown location categories (for example, food and nightlife). Does foursquare have anything like this tied into their system? They should if they don’t.

There’s only maps for San Francisco, New York City, and Pittsburgh right now, but I’m sure there are more to come.

Want more on the clustering behind the maps? Here’s the paper [pdf].livehoods_icwsm12livehoods

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The Garbage Cans are Watching You

An advertising company, called Renew, is receiving backlash from inhabitants of London, for the sensors they have put in their “smart” trash bins. The bins, dubbed “smart” bins, measure the Wi-fi signals emitted by peoples cell phones and after multiple trash bins pick up the same wi-fi signal, the bins are able to determine the route that users took. Citizens of London have hit back and on August 12th officials demanded that all the bins be removed from the streets for personal privacy reasons.

Renew’s chief executive, Kaveh Memari, defends his product, explaining that the bins can track phone signals and recognize the same phones, but cannot determine who’s phone it is or any other personal message. The idea would be similar to web “cookies”. This is the tracking of files that follow internet users through the web, and with these trash cans using phone signals to identify users, Memari hopes that he can “cookie the street.”

Trash bins track peoples smart phones

Trash bins track peoples smart phones

This is something that could provide the opportunity to track people occupying public transport or track people at stops. This could provide the potential to know, for example, where a certain user enters a bus and where he gets off. This could potentially allow the buses to identify patterns between users to be able to identify more acceptable bus routes.

Monitoring People by detecting cell phone

The intent of this post is to give potential ways of tracking people on CTA buses. There is currently no way of tracking individuals, nor who gets off at what stations. With the hopes of providing a prototype for a system that will give the CTA a more comprehensive analysis of their riders, several factors will be necessary.

1. There needs to be an accurate way to detect when a person boards the bus as well as when they get off.

2. There needs to be a way to detect multiple people and keep track of them as they shift through the bus.

The wide spread use of cell phones would offer one way of tracking peoples movements. Cell phone detectors are common in prisons as cell phones have become one of the largest contraband items in prisons. These systems are relatively inexpensive and they are non intrusive, meaning they detect the radiation given off by phones and do not actually monitor phone usage. This second part is important because it allows us to track people without illegally monitoring their calls, texts, or data streaming. Here are links to the two most commonly used companies for cell phone detection.

http://www.cellbusters.com/cell_phone_detector_zone_protector/

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This is geared more towards protecting a wifi network but it still is capable of detecting cell phone uses anywhere from 5-100 ft. If hacked and set with an aduino circuit board this would give the opportunity to monitor the radiation given off by cell phones with reference to location on the bus.

The second link is  to the far more common BVS systems cell phone detector

http://www.bvsystems.com/Products/Security/PocketHound/pockethound.htm

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This system is as small as a deck of cards, which would be advantageous on a crowded bus. This detection is up to 75 ft which allows it to detect anywhere on a bus. If properly used this could allow for detection of peoples cell phones when they get on the bus and when they get off, essentially allowing to track multiple people and to better determine what are the busiest bus stops.

The suggested prototype for this will not give the information on what the best bus routes but it will give the potential prototype for collecting the information necessary to determine this in the future.

The last link is for a tiny homemade motion detector. This is an alternative to cell phone detection but it would be difficult to get a comprehensive understanding of the amount of people on the bus as it monitors motion and cannot determine individuals

http://www.instructables.com/id/Cheap-Wireless-Motion-Sensor-Device/

Craigslist rentals and boundary layers API on rentrent.org (Alpha)

This API originated from my ‘Craigslist Rentals on Map’ websitewww.rentrent.org

As you can see, I haven’t put a lot of efforts to make this site pretty. I just wanted to make it usable. Making this API public is an effort to encourage others to create better websites than mine.

This API takes away the pain of crowling, mining, geocoding and indexing Craigslist data and provides very simple web service calls to fetch the data. This way you can focus on creating a great rentals classifieds application without worrying about GIS bit of it.
You can use this API with Google Maps, Microsoft Bing maps, Yahoo maps etc.


The API supports 2 calls:

Ads.aspx

Service URL: http://www.rentrent.org/RENT/Ads.aspx

Parameter Description
xmin Longitude (min)
ymin Latitude (min)
xmax Longitude (max)
ymax Latitude (max)
bd Number of Bedrooms
ba Number of Bathrooms
type 1: For room rentals
2: For apartment and houses
maxrecords If not passed, maxrecords is set to 250.
If you pass maxRecords=1500,
you can retrieve bulk data using one request.
throwErrorIfOverLimit If not passed, this is ‘true’
You can set throwErrorIfOverLimit=false to get the top ‘maxrecords’ instead of error.
callback Name of a javascript function you want to be called back.

Example URL:
http://www.rentrent.org/RENT/Ads.aspx?xmin=-118.01925659179687&ymin=33.71948521132481&xmax=-117.68142700195314&ymax=33.85644642218431&bd=&ba=&pets=-1&type=2&throwErrorIfOverLimit=false&callback=xxx

The output will be in JSON format. (If you need specific API, send an email on rentrentorg@gmail.com and I will try to speed up the documentation process.)


Map.aspx

Service URL:http://www.rentrent.org/BUY/Map.aspx

Parameter Description
TID Tile ID or Quad Key. (%4 in VE map)
GridX X value of a tile (For google map)
GridY y value of a tile (For google map)
GridZ Zoom level (For google map)
Layer Name of a layer

1. Neighborhoods
2. ElementarySchoolDistricts
3. SecondarySchoolDistricts
4. UnifiedSchoolDistricts

 

Example URL:

http://www.rentrent.org/BUY/Map.aspx?TID=0230121301213&Layers=Neighborhoods

hhttp://www.rentrent.org/BUY/Map.aspx?TID=0230121333&Layers=UnifiedSchoolDistricts

License/Disclaimer/Terms of Use:http://www.rentrent.org/BUY/Disclaimer.html

GE Make Strides Towards “Conductorless” Trains

Projections based off current freight rail growth suggest freight traffic will increase 90% by 2035. Although the rails need to accommodate for this growth, there is still only so much space to expand the rails. GE has been creating solutions to reduce traffic between freight cars and reduce the projected increase in pollution. GE’s Trip Optimizer system strives to use data affiliated with existing train types and train rails to reduce fuel efficiency. Most emissions in freight rails coming from speed variations and standing trains and Trip Optimizer seeks focus on constant, consistent travel, rather than speed, which can lead to traffic jams and track holdups. The Trip Optimizer system studies the typical speeds of engineers on routes and then takes the chooses more gradual changes in speed for the same routes. This reduces average moving time speed, but significantly reduces waiting time and energy used.

The Trip Optimizer system studies the typical speeds of engineers on routes and then takes the chooses more gradual changes in speed for the same routes.

The Trip Optimizer system studies the typical speeds of engineers on routes and then takes the chooses more gradual changes in speed for the same routes.

This technology communicates with GE’s RailEdge technology. This technology communicates with other trains in the network to avoid intersections and traffic jams. This system is made to compensate for any speed lost from the Trip Optimizer. The CEO of GE Transportation explains, “RailEdge optimizes the railroad resources that are already in place — something that only technology can truly help us achieve.” Through these systems freight routes and exact speeds are determined and given to the engineers onboard live.

RailEdge Console

 

Worldwide GIS database

A worldwide map containing downloadable geolocated, data-rich shapefiles is now available on the OpenStreetMap site. The information is exported as an .osm file, a type of XML file that can be read and dynamically imported by CityEngine. While the information contained in the geo-database consists of base layers from mapquest (street centerlines, street widths, building footprints and heights, religious centers, hospitals, bike paths, public transportation lines, parks, etc.), it is a useful resource for generating data-rich base models from actual conditions. A tutorial describing how to import this file type (among other file types) and generate city-wide 3D geometry in CityEngine can be found here:

Collecting Data by sensors / The role of Infrared Cameras in Mold inspections

Mold Problem = Moisture Problem <= Environment Problem <Living condition>

Thermal Imaging has now been in place for a number of years within the building industry and has been used to find problems with building materials, such as: hidden water leaks, leaks within the HVAC system, general plumbing leaks and faulty electrical and mechanical systems. For example, thermal imaging has been successfully utilized to help determine whether or not there are any significant energy losses due the  incorrect amount of insulation or even missing insulation, thermal imaging has also been used successfully to help locate loose electrical connections or overheated breaker boxes by identifying “hot spots”.

While this equipment cannot readily detect mold, it does hold the “key” to finding mold by quickly and accurately identifying the conditions necessary for mold to be present. By identifying variances in surface temperature, thermal imaging has the ability to help us quickly find moisture incursions. The variances in the surface temperatures often mean that there has recently been a moisture incursion and that variance in the temperature of the moisture present behind the surface is affecting the surface temperature, which is easily detected by high quality infrared cameras. Thermal imaging cameras have quickly become the “must have tool” for IAQ Professionals as they can help to expedite the investigation process significantly, thereby providing investigators with opportunities to increase the number of investigation that can be completed on a daily basis.

This video tells us about the process of detecting by using the thermal camera

References: http://www.advancedmoldinspectors.com/thermal_imaging.htm