things might affecting neighborhood definition

Discussions about the traits of strong downtowns and what makes them succeed usually focus on larger cities such as Vancouver, BC, Portland, OR, New York, NY or Charleston, SC. However, a lot can also be learned by looking at things on a smaller scale. This happened to the authors, when we recently looked at downtowns in two small Wisconsin communities. What we learned from them is applicable to many other communities of comparable size.

Our experiences in these two communities certainly confirmed that two basic and broadly held revitalization tenets are just as applicable to small communities as they are to large ones: the need for a comprehensive approach to downtown revitalization and the need to focus on leveraging existing assets. The focus here will be on three other topics that evidence these tenets and deserve our attention:

• The surprisingly complex economic development challenges that many small downtowns typically face
• Providing jobs, especially in more rural areas, is a chronic and seemingly intractable problem
• These small communities too often lack the resources and full range of professionals to initiate and manage broad economic changes.

we again found an economy with numerous economic components and related markets that would have to be analyzed:

•  Retail and restaurants
•  Personal services
•  Educational facilities
•  A medical clinic
•  A seniors’ home
•  A high tech manufacturer

Complex Land Use and Transportation Issues. Even more surprising than the number of markets we had to investigate in Sherwood and the depth of the analyses they required were the complex land use and transportation issues that were hurting the downtown:

• A high degree of dispersion that might be more readily expected in a larger, more urban community. Even with its small population, Sherwood has four commercial nodes including a growing highway node that intercepts a lot of residents before they reach the downtown and where significant new businesses want to locate, e.g. a supermarket, a childcare center, restaurants. There is really poor economic agglomeration, and in a small economy economic assets benefit even more from agglomeration
• The downtown is “unfriendly” to pedestrians – it lacks “walkability.” It has significant traffic with lots of trucks. It lacks a solid building wall front and adequate parking spaces. Many of its businesses are closed to shoppers during the day
• An inability to benefit from a nearby “captive market.” Access to an abutting popular state park was changed so visitors no longer had to drive through the downtown – or Sherwood
• An underdeveloped local roadway system that does not bring residents in newer parts of town naturally to the downtown. Also, the State recently proposed a highway expansion through the heart of downtown, which would have demolished several businesses and undermined what little pedestrian activity currently exists.

our team found a number of complex land use and transportation issues to address. However, unlike Sherwood, which faces growing pains associated with exurban growth, Village X is facing strong, complex and seemingly intractable challenges, characteristic of other small, often more rural communities and their downtowns:

• Its region is sparsely populated and has little or no growth
• The regional economy has long been problematic
•  Attracting or creating firms that can provide new jobs is tough.

http://www.danth.com/category/commercial-nodes

Characteristics and Guidelines of Great Neighborhoods

http://jpprojectthree.blogspot.com/2013/01/define-liveable-neighbourhood.html

A neighborhood can be based on a specific plan or the result of a more organic process.

Neighborhoods of different kinds are eligible — downtown, urban, suburban, exurban, town, small village — but should have a definable sense of boundary.

Neighborhoods selected for a Great Neighborhood designation must be at least 10 years old.

Description of the Neighborhood

It is important to identify the geographic, demographic, and social characteristics of the neighborhood. Tell us about its location (i.e. urban, suburban, rural, etc.), density (i.e. dwelling units per acre), or street layout and connectivity; economic, social, and ethnic diversity; and functionality (i.e. residential, commercial, retail, etc.). We also want to know whether a plan or specific planning efforts contributed to or sustained the character of the neighborhood, or if the neighborhood formed more organically and not through a formal planning process.

Neighborhood Form and Composition

How does the neighborhood …

• Capitalize on building design, scale, architecture, and proportionality to create interesting visual experiences, vistas, or other qualities?
• Accommodate multiple users and provide access (via walking, bicycling, or public transit) to multiple destinations that serve its residents?
• Foster social interaction and create a sense of community and neighborliness?
• Promote security from crime is made safe for children and other users (i.e. traffic calming, other measures)?
• Use, protect, and enhance the environment and natural features?

Neighborhood Character and Personality

How does the neighborhood …

• Reflect the community’s local character and set itself apart from other neighborhoods?
• Retain, interpret, and use local history to help create a sense of place?

Neighborhood Environment and Sustainable Practices

How does the neighborhood …

• Promote or protect air and water quality, protect groundwater resources, and respond to the growing threat of climate change? What forms of “green infrastructure” are used (e.g., local tree cover mitigating heat gain)?
• Utilize measures or practices to protect or enhance local biodiversity or the local environment?

Great Neighborhoods – Characteristics and Guidelines for Designation

A neighborhood can be based on a specific plan or the result of a more organic process. Neighborhoods of different kinds are eligible — downtown, urban, suburban, exurban, town, small village — but should have a definable sense of boundary. Neighborhoods selected for a Great Neighborhood designation must be at least 10 years old.

Characteristics of a Great Neighborhood include:

1. Has a variety of functional attributes that contribute to a resident’s day-to-day living (i.e. residential, commercial, or mixed-uses).

2. Accommodates multi-modal transportation (i.e. pedestrians, bicyclists, drivers).

3. Has design and architectural features that are visually interesting.

4. Encourages human contact and social activities.

5. Promotes community involvement and maintains a secure environment.

6. Promotes sustainability and responds to climatic demands.

7. Has a memorable character.

Description of the Neighborhood

1. When was the neighborhood first settled?

2. Where is the neighborhood located: in a downtown, urban area, suburb, exurban area (i.e., on the fringes of a metropolitan area), village, or small town? What is the neighborhood’s approximate density (e.g., in dwelling units per acre, or other)?

3. What is the neighborhood’s location, its physical extent, and layout?  What are the boundaries of the neighborhood? Are these boundaries formal, defined by an institution or jurisdiction (i.e., wards or other political boundaries, neighborhood associations, other entities) or is the neighborhood defined informally?

4. How large a geographic area does the neighborhood encompass (number of blocks, acres, or other measurement)?

5. What is the layout (e.g., grid, curvilinear) of the streets? Is there street connectivity; is it easy to get from one place to another by car, foot, or bike within or beyond the neighborhood without going far out of one’s way?

6. What is the mix of residential, commercial, retail and other uses?

7. What activities and facilities support everyday life (e.g., housing, schools, stores, parks, green space, businesses, churches, public or private facilities, common streets, transit, etc.)?

8. Is there diversity amongst the residents, including economic, social, ethnic, and demographic? Describe the neighborhood’s homogeneity or heterogeneity in those terms.

9. How has a plan or planning contributed to or sustained the character of the neighborhood? Or did the neighborhood form more organically and not through a formal planning process?

Guidelines for Great Neighborhoods

1.0 Neighborhood Form and Composition

1.1 Does the neighborhood have an easily discernable locale? What are its borders?

1.2 How is the neighborhood fitted to its natural setting and the surrounding environs?

1.3 What is the proximity between different places in the neighborhood? Are these places within walking or biking distances? Does walking or bicycling within the neighborhood serve multiple purposes? Describe (access to transit, parks, public spaces, shopping, schools, etc.). How are pedestrians and bicyclists accommodated (sidewalks, paths or trails, designated bike lanes, share-the-road signage, etc.).

1.4 How does the neighborhood foster social interaction and promote human contact? How is a sense of community and neighborliness created?

1.5 Does the neighborhood promote security from crime, and is it perceived as safe? How are streets made safe for children and other users (e.g., traffic calming, other measures)?

1.6 Is there consistency of scale between buildings (i.e., are buildings proportional to one another)?

2.0 Neighborhood Character and Personality

2.1 What makes the neighborhood stand out? What makes it extraordinary or memorable? What elements, features, and details reflect the community’s local character and set the neighborhood apart from other neighborhoods?

2.2 Does the neighborhood provide interesting visual experiences, vistas, natural features, or other qualities?

2.3 How does the architecture of houses and other buildings create visual interest? Are the houses and buildings designed and scaled for pedestrians?

2.4 How is local history retained, interpreted, and used to help create a sense of place?

2.5 How has the neighborhood adapted to change? Include specific examples.

3.0  Neighborhood Environment and Sustainable Practices

3.1 How does the neighborhood respond to the growing threat of climate change? (e.g., local tree cover mitigating heat gain)?

3.2 How does the neighborhood promote or protect air and water quality, protect groundwater resources if present, and minimize or manage stormwater runoff? Is there any form of “green infrastructure”?

3.3 What measures or practices exist to protect or enhance local biodiversity or the local environment?

The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City

Studying the social dynamics of a city on a large scale has tra- ditionally been a challenging endeavor, requiring long hours of observation and interviews, usually resulting in only a par- tial depiction of reality. At the same time, the boundaries of municipal organizational units, such as neighborhoods and districts, are largely statically defined by the city government and do not always reflect the character of life in these ar- eas. To address both difficulties, we introduce a clustering model and research methodology for studying the structure and composition of a city based on the social media its res- idents generate. We use data from approximately 18 million check-ins collected from users of a location-based online so- cial network. The resulting clusters, which we call Livehoods, are representations of the dynamic urban areas that comprise the city. We take an interdisciplinary approach to validating these clusters, interviewing 27 residents of Pittsburgh, PA, to see how their perceptions of the city project onto our findings there. Our results provide strong support for the discovered clusters, showing how Livehoods reveal the distinctly charac- terized areas of the city and the forces that shape them.

http://videolectures.net/icwsm2012_cranshaw_city/

Mapping Foursquare Check-Ins using QGIS

Here are some simple steps I used to map the Foursquare check-ins:

1) Doing  simple Foursquare search using the venue explore API, I selected coffee shop check-ins, using coordinates for The Loop within a 5000 meter distance.  These are the response results Apigee Snapshot: Foursquare The Loop Coffee Shop Check-Ins.

2) In order to geolocate these check-ins in QGIS, I had to import the JSON into Excel, filter and parse the information to show, name of coffee shop, latitude, and longitude.

3) Import the CSV file to QGIS

These maps are showing coffee shop check-ins in The Loop with purple dots.

 

IBM SMARTER CITIES

What is a Smarter City?

Infrastructure. Operations. People.

What makes a city? The answer, of course, is all three. A city is an interconnected system of systems. A dynamic work in progress, with progress as its watchword. A tripod that relies on strong support for and among each of its pillars, to become a smarter city for all.

Smarter cities of the future will drive sustainable economic growth. Their leaders have the tools to analyze data for better decisions, anticipate problems to resolve them proactively and coordinate resources to operate effectively.

As demands grow and budgets tighten, solutions also have to be smarter, and address the city as a whole. By collecting and analyzing the extensive data generated every second of every day, tools such as the IBM Intelligent Operations Center coordinate and share data in a single view creating the big picture for the decision makers and responders who support the smarter city.

http://www.ibm.com/smarterplanet/us/en/smarter_cities/overview/index.html

Murders in Chicago

Map and statistics of 2012 and 2013 homicides:

http://crime.chicagotribune.com/chicago/homicides

Are you Forming Neighborhoods in Chicago?

The city of Chicago maps out all different types of boundaries, but when it comes to neighborhoods the borders are up to residents. They are constantly changing, what a neighborhood is called, neighborhoods are broken into a number of smaller neighborhoods. How does all of this affect urban design? For example, in 1920, Edgewater was considered to be Uptown on the community area map.  In 1980, the Edgewater community council convinced the city to change this and draw a line between Edgewater and Uptown. Today, people consider Edgewater to consist of six communities, Edgewater, Edgewater Glen, Magnolia Glen, Edgewater Beach, Andersonville, Lakewood, Balmoral. These are neighborhoods within neighborhoods.  How can we use data and the public, users of these neighborhoods, to help us understand the changing boundaries and needs of city? An original survey was done in 1978, asking residents what they call their own neighborhoods, listed 178 neighborhoods, what would this look like today?

The point is that neighborhoods are not homogenous boundaries, they are constantly changing.  The first map shows hard boundaries lines for neighborhoods when in reality these are blurred. The second map start to illustrate the soft edges of boundaries through race and ethnicity.

Source:

http://www.wbez.org/series/curious-city/question-answered-how-are-chicago-neighborhoods-formed-103831

 

Crime in Chicago

Crimes in the communities of Chicago

Link: map of the neighborhoods of chicago and the geography of the crimes in the city

http://crime.chicagotribune.com

CTA Bus transportation

The Bus Network:

http://ctabustracker.com/bustime/home.jsp

The bus tracker API

The Bus Tracker API allows developers to request and retrieve data directly from BusTime (the system which produces estimated arrival times and which provides location and route information in real-time).

What data is available through the API?

Data available through the API includes:

•  Vehicle locations
•  Route data (route lists, stop lists geo-positional route definitions, etc.)
•  Prediction Data
•  Service Bulletins

How does the Developer API work?

• The developer API uses the same data from the BusTime system, which powers CTA Bus Tracker. Information about the location, direction and status of CTA buses is fed from each bus and delivered to the BusTime system, which then can show where buses are or estimate arrival times to stops ahead of a bus.
  Data is updated about once per minute, and arrival estimations are based on how long it normally takes for a bus to get from one place to the next. Because traffic conditions and other unexpected delays occur, we can‖t predict precisely when a bus will arrive—only estimate based on normal travel times during the time of day where an estimate is occurring.
  In order to use the API, the user must sign in to their CTA Bus Tracker account and then request an API key. Only one key will be available per account. Once the request has been approved, the user will be sent an e-mail will be sent to the user containing the API key.

The request that are possible to do with the API technology

• Delayed Vehicle – The state entered by a vehicle when it has been determined to be stationary for more than a pre-defined time period.
• Direction – Common direction of travel of a route.
• Off-route Vehicle – State entered by a transit vehicle when it has strayed from its scheduled
  pattern.
• Pattern – A unique sequence of geo-positional points (waypoints and stops) that combine to form the path that a transit vehicle will repetitively travel. A route often has more than one possible pattern.
• Route – One or more set of patterns that together form a single service.
• Service Bulletin – Text-based announcements affecting a set of one or more services (route,
  stops, etc.).
• Stop – Location where a transit vehicle can pick-up or drop-off passengers. Predictions are only generated at stops.
• Waypoint – A geo-positional point in a pattern used to define the travel path of a transit vehicle.

The CTA state:

The Chicago Transit Authority (CTA) operates the nation’s second largest public transportation system–a regional transit system that serves the City of Chicago and 40 neighboring communities. CTA provides 1.64 million rides on an average weekday, accounting for over 80% of all transit trips taken in the six-county Chicago metropolitan region.Presently, CTA service is provided by two modes: bus and rail.Most rides on CTA are taken by bus. Our bus system consists of 140 routes. Buses make over 25,000 trips daily, and serve nearly 12,000 bus stops throughout the region.

Bus routes in Chicago:

http://www.transitchicago.com/travel_information/bus_status.aspx

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.

Introduction

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.

Methodology

Alternatively, item-based collaborative filtering invented by Amazon.com (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.