In local, most companies are still playing with cities and states as the base of their main keywords.  For example, every yellow pages site out there has a URL called “New York City Restaurants” but not nearly as many are targeting neighborhood-specific queries.  If you search Google right now, you’ll see about 1.2MM URLs in its index that target “New York City Restaurants”, but only about 4,000 that target “Tribeca Restaurants”.  And if you looked for URLs that targeted similar queries for zip codes or nearby landmarks, you would see similar small numbers.

So what does this mean to you?  One of the keys to playing the long-tail local SEO game is to expand your “keyword footprint” by providing URLs on your site that target niche queries such as neighborhood and zip code searches.  And the fewer sites that target these queries, the easier it is to rank well for them.  Now it can be tricky to add that much content to your site and get it indexed and ranked properly, but if you do not have the content, you have no chance of ranking.

In mobile, we are seeing Google in particular showing results in tighter and tighter clusters around the location of the mobile browser.  This means that to rank well for mobile queries your website/URL needs to be showing signals to Google that it is in fact close to the location of the person with the phone.  One way to do this is to use highly specific keywords that reference the desired location such as a neighborhood.

While SEO is an ever-evolving game, if you don’t have the data, you’re not even in the ballpark.

LocalSEOGuide is Andrew Shotland’s blog about local search engine optimization and local marketing trends.  Andrew provides “national” and local seo services to enterprise-level sites, startups and small businesses around the world.

We all know that the number of online and mobile resources for real estate search have proliferated in recent years but it takes consumers time to change their habits and adopt new technology.  It appears that Web resources are now firmly embedded as the primary way for people to search for residential real estate—in fact more than 80% of respondents reported using online tools.

In a story written by the Philadelphia Inquirer about the survey, they point out that the number one online resource for real estate search were property portals and noted that “The big three among these sites were Realtor.com (64 percent), Zillow (61 percent), and Trulia (51 percent).”

We are of course pleased that the top sites all leverage Maponics location datasets to enhance search capability and provide local geographic context for areas such as neighborhood, school and ZIP Code Boundaries.  Visit our customer use case pages to read about how one of these companies uses our data.

And this summer, we will begin offering a first-of-its kind dataset containing subdivision boundaries across major metro areas.  Read more about Subdivision Boundaries.

From our Subdivision Boundaries announcement:

In suburban settings, the smallest defined area around properties is typically the subdivision—which can include everything from a few homes within a gated community to a development with hundreds of properties.  The real estate industry has long recognized that the immediate area surrounding properties significantly impacts quality of life and home values.

In the Inquirer article, they also note that driving through neighborhoods is still a common house hunting practice, although people now use online resources to winnow the list of properties they visit—saving money and precious time.  The good news here is that in addition to using our data to enhance their web sites, many of our customers also embed our data in their mobile apps to provide enhanced location context for home buyers on the go.

For more ideas about how mobile real estate apps could add cool new features based on Maponics geofences, check out this article on Directions Magazine.

The neighborhood dataset available on our API is truly unique in its size and scope.  As of the end of 2010, it contained more than 138,000 neighborhoods in thousands of cities across the globe.  That makes it the largest database of neighborhood boundary definitions ever compiled.

Here is a summary of the data available in this extensive neighborhood dataset:

• Neighborhood Name
• Neighborhood Boundary
• Neighborhood Centroid
• Neighborhood Classification*
• Country
• County
• CBSA
• MCD
• Place

*- Data available for a subset of records.

With the combination of core data fields that contain neighborhood information along with geographic reference information (e.g. Metro) about where the neighborhood is, this dataset offers a comprehensive geo stack for developers looking to add social and cultural context.

See full dataset details here.

Developers can use a variety of methods to access neighborhood data through the API.  They can be broken down into methods that:

• return attributes of spatial objects (getGeoAtt)
• return an ID of a single spatial object (getGeoID)
• return a list of IDs for multiple spatial objects (getGeoList)
• return geographic coordinates of spatial objects (getGeoPoly)

In terms of input parameters, requests can include a simple coordinate pair (lat/lon) per request or a batch of coordinates within a single request.

With such an extensive dataset and the numerous methods to access it, the Maponics Spatial API represents a powerful addition to the developer’s toolkit.

Contact us if you would like to set up a trial for the Maponics Spatial API.

The Maponics Spatial API reduces both time-to-market and implementation costs and makes it easy for organizations to add and improve geographic context.  With just a few lines of code, developers can leverage unique and meaningful map layers for reference, filtering and display.

Current and relevant data is central to many web and mobile services but managing the data on the back-end presents serious challenges.  Most companies don’t work with geographic data on a regular basis and therefore don’t have the skills or tools to do so effectively.  Further, obtaining, normalizing and integrating data from various sources is labor and time-intensive and must be repeated each time the data is updated.  Alternatively, requesting only the data needed by visitors/users—when they need it—(via an API) offers huge advantages.

The Maponics API is a RESTful web service that accepts simple requests in the form of plain text XML.  It supports a host of methods that include everything from a simple query containing a single lat/lon to large batches requests.  It can also return everything from simple attribute data to full sets of coordinate pairs (polygons) that represent things like Neighborhood Boundaries.

In coming weeks, we’ll add posts to this series that feature each of the datasets available through our API and get down to the nitty-gritty details about fields, use cases and methods.

But, if you can’t wait that long, check out the API datasets page for a complete list of the datasets available.  On this page, you’ll find details about which fields are available for each dataset and the methods that can be used to access them.

• Coverage of locally-sourced neighborhoods has increased by over 10,000 polygons with coverage expansion in more than seventy new cities, including Broomfield, CO, Rocklin, CA, Blue Springs, MO and Prince George, BC.  As of this release, coverage in North America includes over 120,400 neighborhoods in 49 states, in the District of Columbia and Canada.

• Washington, D.C. and the Miami, FL metros were a focus of existing coverage expansion, with more than 1,000 neighborhoods added in each of these metro areas.

• As is true for all Maponics Neighborhood Boundaries product releases, U.S. Census files are included at no additional charge.  Starting with the 1.11.1 product release, Canadian Census files and accompanying product documentation are also included at no additional charge.  The Canadian Census files include Provinces, Divisions and Subdivisions.

In addition to the update to North American neighborhoods, we also released our latest version of Neighborhood Boundaries in Europe, adding over 1,000 new neighborhoods in 15 new cities.

Check out the new release on our neighborhood boundaries demo page or contact us for a neighborhood data sample.

Enjoy and feel free to post comments below.

Do you want to learn about the value of presenting your content and services at the hyper-local neighborhood level?

Join Maponics and Digital Map Products on November 17, 2010 for a free webinar:

Hyper-Local ROI: The Business Case for Search & Display at the Neighborhood Level

We’ll discuss how to make the business case for adding new hyper-local search and mapping display options to your site or mobile app. The webinar will include real use cases, implementation tips, and examples of neighborhood boundaries in action with map displays.

Learn more and register here.

Spaces are limited so please sign up early!

In many ways, a geofence is simply another name for something that has been around for a long time— a geographic boundary (i.e. polygon) defined by a series of lat/long coordinates.  The term geofencing refers to the use of geofences in combination with a location-aware device running apps/services that can send notifications when the person carrying the device crosses a geofence.  And of course, Maponics is all about defining geographic areas and delivering them in GIS data products, so we are excited about the new ways in which they can be leveraged and applied in geofencing scenarios.

We see geofencing use cases for all of our GIS data products but I wanted to highlight several here to make the case for how predefined geofences, versus basic radius perimeters, can be more meaningful and useful in geofencing applications.

Predefined Geofences vs. Radius-based Geofences

Most of the scenarios you might be reading about in the tech sites hypothesize about a geofence that is a simple radius around a location, like a store.  A radius geofence may be used in push-marketing situations where a store owner hopes you’ll cross her geofence so that her marketing alert will go off and send you a promotional offer to encourage you to visit her store and purchase merchandise.  We call these “on the fly” geofences because as a user, you never know when you might randomly cross one or multiple geofences that are generated on the fly—the stores themselves (or their marketing companies) control when users get the notices.  As a user, this can be pretty noisy and we guess few users will opt in to such a service.

Contrast this with predefined geofences, for example, neighborhood boundaries.  As a user, you could set your preferences to only notify you when there are restaurants offering specials in the Russian Hill neighborhood of San Francisco.  Of course, this requires a predefined geofence for Russian Hill.  Such a geofence is not a radius –it is a complex polygon, shaped by the streets or natural borders of said neighborhood.

As you’ll see, there are numerous reasons why predefined, polygon-based geofences are going to be superior in many situations.

Social Networking

A sense of location and movement is becoming a fundamental part of social networking.  Much of the emphasis has been on tagging updates with location information (read about how Maponics powers place designation for geotagged Tweets) but generating alerts and Tweets based on when your friends enter or leave areas, like choice neighborhoods, can give you a heads up about where the party is headed.  Also, using neighborhoods boundaries as geofences enables alerts based on when friends enter the neighborhood where you are hanging out.

Real Estate

Mobile apps and house hunting go well together.  Consider the following scenario:

A couple with two kids in elementary school is planning to relocate to a new city for career opportunities.  Prior to their house hunting trip, they visit an online real estate web site and generate a short list of properties to visit based on numerous preferences, including price range, number of bedrooms/bathrooms, amenities, etc. within several neighborhoods that they heard have good schools and are kid-friendly.  Further, on the real estate web site, they sign up for a geofencing service to be notified on their Smartphone (the house hunters tool of choice) when they enter and exit their target neighborhoods so they can get a sense of locally-recognized neighborhood boundaries and to help orient and direct their house hunting activities.

The couple uses the online real estate site’s mobile app on their iPhone to get directions to the first few properties.  While looking at one of the houses, they visit a nearby park with their kids and strike up a conversation with a local mom.  They quickly find out that there are several public elementary schools nearby but they are not at all on the same level as far as GreatSchool ratings (also available through the mobile real estate app—and that the attendance areas for the schools cut across several of their target neighborhoods. Armed with this local “intel”, they launch the online real estate app once again and find to their surprise that schools and even school attendance zones are search parameters they can use to further refine their target house list.  Similar to the neighborhood alerts they set earlier, they turn on the geofencing service for elementary school attendance zones so that they know as they drive around when they enter and exit the exact boundaries that define which public schools residents attend.

The couple’s location-aware, micro-targeting saved them hours of running around wasting time looking at houses that on the surface may have looked fine but ultimately would not have worked because of the location in relation to desired neighborhoods and schools.

Parental Notification

Many parents grapple with striking the right balance when considering technology that can help them monitor their kids’ location and activities.  Lots of kids now have GPS-enabled phones, so it is easier than ever to track them–but, exact location tracking is often not an option because kids reject it as too intrusive.  With neighborhood-based geofencing, parents can get notifications if and when their kids enter certain neighborhoods—without monitoring their exact location.  This might be just enough information for parents to know if they need to touch base or take action but not too much to be considered draconian.

We’d love to hear your cool geofencing ideas—feel free to post comments below.