Laster semester we utilize two kinds of clustering  algorithms to do our analyze. The first one is distance based clustering, the second  one is grid based clustering. Although logically they are very similar, both of them are forming clusters based on distances, they are different in doing this, and results can be different. Below is the logic of these 2 algorithms.

A. distance based  clustering:

1. Buffering every single points with a distance which can be set by analyzers.

LB cluster1

2. Merging circles which have larger overlaps than the setting number into clusters.

LB cluster2

B. Grid based clustering

1. Set the distance of grid lines. Divide the target area by grid.

grid cluster1

2. Locate points into cells, then look at neighbor cells of target cell. If there is point in theses neighbor cells, merge these points as core of a cluster.

grid cluster2

3. Making convex hulls based on these cores of cluster. There is a parameter through which you can control the size of clusters.

grid cluster3

Blow is the SQL for Grid based clustering

WITH clstrtags AS ( SELECT *, tag.geom as tgeom FROM gridcluster(30,’urbantag’,’geom’) as grid
JOIN urbantag as tag
ON st_contains(st_setsrid(grid.geom,3435),st_setsrid(tag.geom,3435))
ORDER BY rid,cid
counts AS (SELECT count(tagid) as count, clusterid, activity FROM clstrtags GROUP BY clusterid, activity),
countss AS (SELECT count(tagid) as count, clusterid FROM clstrtags GROUP BY clusterid)

select counts.clusterid, counts.activity as act, counts.count as actct,countss.count as tagid, counts.count/(countss.count + 0.00) as percentage
from counts join countss
on counts.clusterid=countss.clusterid
where countss.count>1
order by clusterid


Parcel the knowledge and experiment from architect


This is the common process of architecture design, the development process is the most logic and time consuming one. So can we build a system in which we can easily input constrains from code, context and our concept, the computer then can generate design based on these constrains?

Algorithem Architecture & Urban

Activity Diagram0

The following 2 links are two examples.,354/

In the future, rely on such tools, we can not only arrange fixed function building programs but also  convertible ones. This technology can make our city more efficient

The following links are two examples.

Cluster analysis

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis used in many fields, including machine learningpattern recognitionimage analysisinformation retrieval, and bioinformatics.

Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties.

Besides the term clustering, there are a number of terms with similar meanings, including automatic classificationnumerical taxonomybotryology (from Greek βότρυς “grape”) and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification primarily their discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals.

Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Tryon in 1939 [1] and famously used by Cattell beginning in 1943 [2] for trait theory classification in personality psychology

Collaborative filtering

collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B’s opinion on a different issue x than to have the opinion on x of a person chosen randomly. For example, a collaborative filtering recommendation system for television tastes could make predictions about which television show a user should like given a partial list of that user’s tastes (likes or dislikes).[2] Note that these predictions are specific to the user, but use information gleaned from many users. This differs from the simpler approach of giving an average (non-specific) score for each item of interest, for example based on its number of votes.


The growth of the Internet has made it much more difficult to effectively extract useful information from all the available online information. The overwhelming amount of data necessitates mechanisms for efficient information filtering. One of the techniques used for dealing with this problem is called collaborative filtering.

The motivation for collaborative filtering comes from the idea that people often get the best recommendations from someone with similar tastes to themselves. Collaborative filtering explores techniques for matching people with similar interests and making recommendations on this basis.

Collaborative filtering algorithms often require (1) users’ active participation, (2) an easy way to represent users’ interests to the system, and (3) algorithms that are able to match people with similar interests.

Typically, the workflow of a collaborative filtering system is:

  1. A user expresses his or her preferences by rating items (e.g. books, movies or CDs) of the system. These ratings can be viewed as an approximate representation of the user’s interest in the corresponding domain.
  2. The system matches this user’s ratings against other users’ and finds the people with most “similar” tastes.
  3. With similar users, the system recommends items that the similar users have rated highly but not yet being rated by this user (presumably the absence of rating is often considered as the unfamiliarity of an item)

A key problem of collaborative filtering is how to combine and weight the preferences of user neighbors. Sometimes, users can immediately rate the recommended items. As a result, the system gains an increasingly accurate representation of user preferences over time.


Collaborative filtering systems have many forms, but many common systems can be reduced to two steps:

  1. Look for users who share the same rating patterns with the active user (the user whom the prediction is for).
  2. Use the ratings from those like-minded users found in step 1 to calculate a prediction for the active user

This falls under the category of user-based collaborative filtering. A specific application of this is the user-based Nearest Neighbor algorithm.


Alternatively, item-based collaborative filtering invented by (users who bought x also bought y), proceeds in an item-centric manner:

  1. Build an item-item matrix determining relationships between pairs of items
  2. Infer the tastes of the current user by examining the matrix and matching that user’s data

See, for example, the Slope One item-based collaborative filtering family.

Another form of collaborative filtering can be based on implicit observations of normal user behavior (as opposed to the artificial behavior imposed by a rating task). These systems observe what a user has done together with what all users have done (what music they have listened to, what items they have bought) and use that data to predict the user’s behavior in the future, or to predict how a user might like to behave given the chance. These predictions then have to be filtered through business logic to determine how they might affect the actions of a business system. For example, it is not useful to offer to sell somebody a particular album of music if they already have demonstrated that they own that music. Considering another example, it is not necessarily useful to suggest travel guides for Paris to someone who already bought a travel guide for this city.

Relying on a scoring or rating system which is averaged across all users ignores specific demands of a user, and is particularly poor in tasks where there is large variation in interest (as in the recommendation of music). However, there are other methods to combat information explosion, such as web search and data clustering.

TweetCity: Building London using Real time feeds and CityEngine


As part of our time here in CASA UCL, Stephan Hugel and I, developed a simple tool to capture and visualize live tweet feeds and project them onto the actual form of the built environment. The application uses the Twitter API, and visualizes results in 3D, similarly to the known London Twitter map by UrbanTick, developed using the data collector created by Steven Gray,  in an attempt to create a new urban landscape.

This work is a first attempt to bring real-time data feeds directly into 3D GIS and 3D cities and visualize them using a different view. As Stephan wrote: “What if London’s buildings grew according to the amount of data they generate?”. There is currently a big emphasis on BIM and data management, with a clear focus on sustainability and social infrastructure. However, there is very little information on how and if this process affects the way the built environment grows and evolves.
Of course this project does not aim to address such questions, but to demonstrate new ways of augmenting already existing spatial data. The application was built entirely in ESRI’s CityEngine, as it offers a range of 3D visualization techniques which relate to the urban environment. CityEngine allows the generation of forms using simple “rules”. In this case, the project collects real-time tweet feeds, aggregates them by a custom discrete zone system and by default, visualizes them as building heights. CityEngine provides the key-advantage of  allowing the automatic storing of spatially geolocated data directly on a shapefile, augmenting GIS with live information and update the visualization real-time. That is why this application, can work with different basemaps, such as a land use map, wards, or even roads.  At the moment, the script stores the number of total tweets for any discrete zone and keeps track of time and date.
For example, if the user wants to know how many tweets will fall in the area of Hyde Park for a specific period of time, he will only have to import an outline of the park in the scene of CityEngine and adjust the values appropriately. The rules that generate geometry are independent of the Information System, a feature which according to my opinion is one of the main advantages of CityEngine, as is allows the customization of different visualizations.
Here is a little video preview, while thanks to Stephan you can download the full script along with instructions and samples from GitHub.
Works with CityEngine, download trial version here.


Hotel within 5 miles of mick

Chicago Hotels within 5 Miles of McCormick Place

Chicago is a global destination with world-class accommodations. Our ever expanding hotel industry can be hard to keep up with, so here’s a list of recent and future openings.


Hyatt Regency McCormick Place – Spring 2013
Phase two of the hotel’s multi-million dollar expansion project is expected to be completed by summer of 2013. The project includes a complete renovation of its 800-guest room tower, expanded seating for the hotel’s restaurant and the construction of a second private dining room. Phase one, which began in December of 2011, included the construction of a 460-room tower that is expected to be completed in the summer of 2013. Phase three of the property expansion, which was completed in June 2013, focused on the hotel lobby, the renovation of the 25,000 square foot Hyatt Conference Center, the addition of 3 new boardrooms, and a new business center.

Hyatt Regency Chicago – Spring 2013
Culminating in April 2013, the hotel will announce the final phase of its complete hotel transformation. Hyatt Regency Chicago has dedicated $168 million to enhancing all aspects of our guest experience, including state-of-the-art rooms, meetings spaces and outlets. The renovation also includes a reimagining of the property’s in-house restaurant, Stetsons Modern Steak + Sushi.

Fairmont Chicago, Millennium Park – Summer 2013
The Fairmont Chicago, Millennium Park, 200 North Columbus Drive, debuts a $2.5 million renovation of the hotel’s 63,000 square feet of meeting space, which now includes 15 meeting rooms ranging from 400 to 16,000 square feet. This project is the culmination of a more than $60 million lobby-to-roof renovation of the entire hotel.

Thompson Chicago – October 2013
On October 1, 2013, the Thompson Chicago will open the doors on a 247 room hotel that includes 12,225 square feet of meeting and pre-function space. Scheduled to open in December 2013 is an on-site restaurant, NICO, as well as an accompanying lounge, NICO Salone.

Langham Chicago Hotel – 2013
The landmark, riverfront Mies van der Rohe building has been transformed into a hospitality haven, with 316 spacious guest rooms; the 9,000 square-foot Chuan Spa; and dining bound to rival Langham brand’s other Michelin Award-winning restaurants. The hotel also provides some of the city’s largest standard guest rooms starting at 516 square-feet, a Mediterranean-influenced restaurant and lounge (Travelle), as well as 15,000 square-feet of meeting and banquet space.

Fairfield Inn & Suites Chicago Downtown – 2013
The nineteen story, 180 room hotel is slated to open in June of ’13. Located in Chicago’s famed River North area, guests will be a short walk away from shopping on The Magnificent Mile and the always energetic nightlife of Hubbard Street.

Hyatt Place Chicago Downtown River North – 2013
Currently under construction, the 212 room hotel is scheduled to open in June of 2013 on Chicago’s bustling and nightlife-heavy Hubbard Street.

Virgin Hotel Chicago – Late 2013 / Early 2014
Eccentric billionaire and founder of Virgin Airlines, Sir Richard Branson, is bringing his new lifestyle brand to Chicago. Virgin Hotels will open a new location in the historic Dearborn Bank Building in the Loop. Specifics on design and amenities have been mum for now, but expect a stylish and elegant establishment fitting of its River North location. Construction began in May on the 250 room hotel, with a projected date of completion in Fall 2013.

Renaissance North Shore Hotel – 2013
Renaissance’s 22,000 square feet of meeting space will be the first to benefit from a $7.5 million total revamp of the North Shore hotel. The Northbrook Ballroom and the Greenery, which boasts floor-to-ceiling windows, and a deck, will receive complete makeovers, giving the hotel a more modern and welcoming feel. Renovations of guest rooms will top off the overhaul.

Aloft Chicago City Center – June 2013
Business meets style at Clark and Illinois streets, where the modern, chic, 272-room hotel will offer delegates the convenient combination of work and play. With free Wi-Fi, plug-and-play connectivity centers in rooms and a state-of-the-art meeting space, guests will enjoy the best of both worlds with the latest business amenities and the swanky hotel bar.


Hotel Indigo – Spring 2014
U.K.-based InterContinental Hotel Groups PLC announced that it will manage a 145-room hotel in a long-vacant Michigan Avenue building just north of Millennium Park. The century old building will be gutted and refurbished to accommodate a state-of-the-art accommodation space, rooftop bar and 8000 square-foot, multilevel restaurant.

The Godfrey Hotel Chicago – 2014
Located adjacent to the Hotel Felix in Chicago’s River North neighborhood, this 16-story upscale boutique will feature stunning architecture and a stylish overall interior. The space comes complete with over 220 rooms, 24-hour fitness center and a 12,000 square-foot rooftop bar and lounge.

SOHO House Hotel – 2014
After spending years in the rumor mill, it’s official. SOHO House is coming to Chicago! The six story private club and hotel will include a full-sized pool, rooftop bar, and three open to the public restaurants.


Loews Hotels & Resorts
Set to open in January 2015, this 52-story tower will feature 390 luxury apartments, 400 guestrooms and 25,000 square feet of meeting space. The property, which will be located a block north of the Chicago River and two blocks east of Michigan Avenue, will also boast a restaurant, fitness center and the ever popular rooftop pool and terrace combination.


McCormick Place Headquarters Hotel – 2016
The Metropolitan Pier and Exposition Authority announce the building of 1200 room headquarters hotel adjacent to McCormick Place. The hotel’s size, facilities and location will allow conventions and meetings to use a single property near McCormick Place as their headquarters, which will be located at the intersection of Michigan and Prairie Avenue. The new headquarters hotel is projected to attract approximately 15 new mid-market events per year to McCormick Place, generating roughly 80,000 additional attendees. The number of hotel rooms within walking distance from the convention center will be doubled.

Chicago Hotel Supply&Demand outlook



With the inclusion of the official overseas visitation volume for 2012, Chicago’s total visitation set a new record high in 2012 at 46.37 million, meaning in just two years, the city is 66 percent of the way to closing the gap and realizing Mayor Emanuel’s 2020 goal of 50 million visitors.  Reaching that goal would increase tax revenues to $1.2 – $1.3 billion per year and nearly $15 billion in direct spending.  Even this single rise in the rankings has helped drive the record hotel construction we have seen so far this year, as well as a record hotel occupancy rate of more than 73% – the best since the recession. That hotel occupancy produced more than $1 billion of hotel revenue so far in 2013.

surpassing all expectations, Chicago welcomed a record 1.369 million overseas visitors in 2012, representing an impressive 14.2% gain over the 1.199 million overseas visitors reported in 2011, and moving Chicago into 9th place in overall city rankings according to US Department of Commerce rankings.

Growth from Asian markets to Chicago (+30%) was particularly strong, twice that of the national average, with strong growth from the South American markets (+18%). These two markets are responsible for more than half a million overseas visitors in 2012.  This outsized growth in these two markets reflect the increased marketing that Choose Chicago has placed there, locating new offices and selling Chicago has a top tourism, convention and business destination for these economic zones. 2012 also saw a dramatic 41% increase in visitations from the Mexican market to 142,000, more than four times the national growth rate.

Tourism includes many interesting issues, I am mainly concerted of what phisical change will the thrive of this industry bring? These issues interest me most:

From Where are overseas tourists  from?

How do they come to Chicago?

What change will this change bring to Chicago? For instant how many hotels, resturants and shopping malls are being built or rebuilt?