Two weeks ago Directions Magazine hosted the 10th edition of its Location Intelligence event. LocationTech, HERE and Oracle Spatial and Graph programs were intertwined with the event. Executive Editor Adena Schutzberg shares her key takeaways.

Two weeks ago Directions Magazine hosted the 10th edition of its Location Intelligence event. LocationTech, HERE and Oracle Spatial and Graph programs were intertwined with our event. Here are my takeaways after sampling sessions from all four tracks.

Big Data, Consumer Sampling and “Elizabeth’s Soap”

Paul Donato (photo at right), the executive vice president and chief research officer at Nielsen, delivered the keynote address. Yes, that’s the same company that does the Nielsen TV and radio ratings. I guarantee anyone reading this recap who attended Donato’s keynote presentation (video)

can tell you the story of Elizabeth’s soap. Why? Because Donato illustrated his key points with a poignant, true story. Here’s my retelling:

Elizabeth is the oldest of three generations in a rural village in Kenya. Each month Elizabeth travels by bus about 100 km to the closest city and buys a big box of soap for washing clothes. She returns and puts the soap into small bags and sells it to her neighbors. If you ask her customers what kind (brand) of soap they use, they’d say, “Elizabeth’s soap.”

That’s important because in developing areas like hers, there is no sense of “brands.” Further, unlike in the developed world where demand drives supply, in these areas supply is what will drive brands and brand loyalty. The challenge for Nielsen is measuring that supply chain. In short, Nielsen has to find out which little stores, like those Elizabeth visits, stock which kinds of soap and how much. Out where Elizabeth shops, 80% of trade is done without electronics, making measurement difficult.

How, then, does Nielsen measure such things for its customers? By hand! Enumerators visit the shops regularly and count how many boxes of each type of soap (and other products) are on the shelves. That’s labor intensive, slow and expensive. By the way, Nielsen has been doing manual counting for some time. Back in the day, its employees counted cans of Heinz beans on store shelves. That’s said to be the origin of the term “bean counter.” I was not able to easily confirm the veracity of the story via the Internet.

But, back to those enumerators. Where should they go to do the counting? Where the stores are, of course! But, how do you know where the stores are? Nielsen uses remote sensing (with training on a “retail signature”) to identify areas without any stores. That can save teams 40% of the cost of enumerating by simply not visiting those areas. Instead, they can concentrate resources on areas with stores.

. That’s the article I read that prompted

Marketing Uses of Location-based Advertising: Location May Play Second Fiddle

, founder and president of the Location Based Marketing Association (

), addressed the indoor marketing space and shared a great example from Guatemala (See video at 9:00 mark). A sport shoe store (Meat Pack)

offered coupons to those entering competitor’s locations

. The trick? The value of the coupon would drop over time from 100% off to zero% off. So, potential bargain hunters were sprinting to the store to get their deals. One fellow did, in fact, receive an 89% discount on his purchase.

Nivea, the skin care company, also used location in a campaign, but in a secondary way ... and outdoors. The company

offered paper “watches” in magazine ads

that parents were to register with a website via the Nivea website. Mom or Dad then cut the “watch” out of the ad and put it on the child’s wrist. The parent could set a radial boundary for the child and if he or she went beyond it, the app’s alarm would sound via the cell phone. The story takes place at the beach, tying together two kinds of protection: Nivea sunscreen and Nivea’s tracker.

What these novel examples suggest is that the buzzworthy and effective location-based campaigns will use location, but only as a small part of the campaign. In short, it’s just one simple element.

Khan also suggested that augmented reality is a new media, the eighth media by those who are counting. I found

. He made that comment in the context of how new media may or may not replace old media. Print may be the exception, he argued, but in general new media does not replace the one(s) that came before, but rather changes it. Thus, TV did not replace radio, but radio changed.

What does it mean if indeed augmented reality is a new media? I for one never thought of it that way. It’s not exactly the first “location-based media” as mobile devices with LBS apps already exist. Still, it has different qualities and potential than LBS. How will this new media play out and, equally important perhaps, how will it impact what came before? How might LBS apps change? How might TV change, or radio, as augmented reality gets wider use?

Washington and Geospatial: Opportunity Knocking

John Palatiello of MAPPS, Kevin Pomfret of the Center for Spatial Law & Policy and

Jules Polonetsky of the Future of Privacy Forum had a brief chat about what’s going on in Washington related to policy and the geospatial industry

(video). Palatiello suggested there are lots of opportunities for geospatial to help with federal government issues. Unfortunately, he observed, decision makers do not always decide to take advantage of our expertise. He noted these opportunities:

Obamacare has some 814 provisions for the use of geodata, but Congress said no to a suggested geospatial officer for the Department of Health and Human Services.

There is no complete federal land inventory and no accurate land building inventory.

A national parcel system (which the U.S. does not have) might have provided warning for the foreclosure crisis.

We need a good map of our digital coast which is home to 52% of the U.S. population as climate change continues.

The Highway Bill needs reauthorization and should include support for geospatial, vehicle-to-vehicle communications and other cutting edge technologies.

We need better maps of underground utilities to help prevent disasters. (See Geoff Zeiss’ discussion of a plan for such a map for France.)

FEMA’s flood insurance program is $24B in debt due to recent weather events; we need data to help reduce risks.

Federal agencies are not coordinated in their collection and use of geospatial data and related assets, according to the Government Accountability Office.

Any discussion of climate change and its impact on this country demands geospatial input.

Federal discussions of privacy and unmanned aerial vehicles (UAVs) use the term “precise geolocation” but it’s still undefined.

Lean In to Transparency When it Comes to Privacy

Polonetsky made a valuable point about how consumers respond to different companies’ use of personal data. When Amazon suggests new books, CDs or shoes that they might like when they return to its website, its customers are delighted. When Target taps the same sort of data and determines

What’s the difference? Amazon is transparent, perhaps overly so, about how it uses customers’ purchase history. Everyone knows that it makes recommendations based on the data and for most people, that feature adds value to each visit. The same is true for the

Disney offers for visitors to its parks. Yes, they collect data about where the family goes, what they buy and what rides they select, but it also adds value: shorter lines, visits with costumed characters and a way to track lost children. Polonestky suggests that retailers and others “lean in” to privacy, by which I think he means embrace the data, use them to add value and be not just transparent, but shout about their benefits for the consumer from the rooftops.

Placed Tracks 150,000 People 24/7 and They Don’t Mind

With all of the efforts to learn about consumer behavior and not breach privacy, what Placed does seems so “old fashioned.” David Shim, CEO,

that the company basically “pays” individuals (about 150,000) to be tracked 24/7 as they go about their lives and use their mobile devices. Payment comes in the form of cash or for those who prefer, via donations to charity. All those people create data about 150M location visits per day. The company has individuals validate about 12M by actively confirming where they went.

Those data, in aggregate, are used to help model how the rest of us might act in general, or react to different ads campaigns or offers.

Jim Steiner (photo at right), vice president for Server Technologies at Oracle, opened the 10

Oracle Spatial Summit with one message to communicate: the latest and greatest version of Oracle Spatial and Graph, version 12C, has dramatic performance enhancements (Steiner's keynote and other slides in PDFs). He cited two key ways his teams achieved speeds up to hundreds of times faster than previous releases:

putting processing closer to the data (see above)

Spatial Vector Accelerator (an option that speeds things up, called by one user “the magic switch”)

When asked to make some predictions about the future of geospatial computing and Oracle, Steiner ticked off these areas to watch:

Social Media, Crime and the Fourth Amendment

AGSI probably had the most “buzz” during the event. The company, based in Canada, offers solutions that make use of publicly available social media data. The company has been particularly successful

taps into the firehouse of data from Twitter, Facebook, YouTube and others (more to come) and allows officers to query over time frames, in a specific geography, for specific keywords. Users can also set up alerts for when certain combinations of terms come up in an area of interest. An alert came up for the mall shooting in Kenya, for example, before local respondents knew what was going on.

Go360 can be used to get a heads-up on planned attacks or to aid in response when one has occurred. It can also be used to find and contact potential witnesses who were in the area at the time of a crime. AGSI demonstrated that by inviting those who tweeted within our area of the conference center to its booth (image below).

AGSI used its Go360 solution to find those who tweeted about our event from the area of the Washington Convention Center. It sent tweets to those who did and invited them to its booth.

C. G. Walwyn, commissioner of the Royal St. Christopher and Nevis Police Force, had nothing but praise for the system. He highlighted how data used to help track down and arrest gang members and thieves could sail into court in the U.S. (he’s worked here, too). Why? The data are all public; there are no issues of privacy invasion.

I asked the obvious question: Why do folks committing crimes leave their location information turned on when posting on social networks? The response: They are not too bright and they like to use that feature for other reasons, such as finding their friends.

The commissioner noted that only a select few in his force have access to the system “because everyone has a best friend.” By that he meant that once word gets out about how the system works, the “not too bright” ne’er do wells on the island will switch off location sharing and make the system less useful. That’s not to suggest that there are not other ways to determine a device’s location; there are. However, heading down those paths can spark fourth amendment issues here in the States. One attendee gave the AGSI’s system about six months of use before those being rounded up in St. Kitts and elsewhere change their online social media behavior to avoid detection.

Andrew Turner, CTO R&D Center at Esri DC, addressed the LocationTech audience to speak about

. His main point was that moving data around or copying the datasets to new locations is not a best practice for maintaining or sharing them. Instead, he argued, the best approach is to leave the data where they are and use the workflows in place to keep them up to date. Then, the trick is to add a layer of technology to publish the data.

He was speaking in particular about the newly launched Esri Open Data solutions in ArcGIS Online, but other options are available. He noted a solution for those who want to host their geodata in GitHub, and publish it to ArcGIS Online. A tool called

enables just that. It exposes GeoJSON services as Feature Services. If I understood correctly, the City of Philadelphia is currently using it.

Oracle’s Jim Steiner and his colleagues also made a push for leaving datasets where they are, but for a different reason: performance. There’s been a move afoot for a few years to leave data “where they are” in the database (compressed or not, in the case of LiDAR stored in Oracle) and bring the processing to the data. It turns out that implementation for processing, in contrast to the reverse (aka “the old way”), is far faster (slides from Oracle LiDAR presentation, PDF).

On the other side of the data discussion was Juan Martin, CTO at Boundless, who

, an open source toolkit for managing changes to data. His argument is that there are no authoritative datasets, but instead datasets are changed for specific uses. Thus, there’s a need to track changes and different versions. That’s what

does. The tool is designed to be robust; it has no single point of failure and is distributed.

The good news is that version 1 of GeoGit is coming this summer. Plans are in place for an implementation in a browser, in a Python library and as a QGIS plug-in. The good news for developers is that most enterprises will need a custom implementation to embed GeoGit into their existing solutions. And, that means more good news for end users: once implemented, most end users’ workflows will look and act the same as before, even as GeoGit runs invisibly inside the enterprise, taking care of data change business. Geoff Zeiss did a nice detailed

that introduced spatial analysis on its platform. One theme raised was that “anyone can make a map, but not everyone can maintain a map.” That’s true in several senses. It’s certainly relevant to new users of spatial analysis as they select base maps/base map services from providers. It’s also an issue in organizations like the Census Bureau, which must keep up with changes to the United States land base by

. And, it’s true indoors, as well. Ankit Agarwal, CEO of Micello,

that his company, known for mapping the great indoors, spends more money updating its maps than creating them in the first place.

Why is a Real Estate Company Dropping PostGIS?

Simon Greener, an independent geospatial database consultant and an advocate for users (and a long-time

) shared that a large real estate company in Australia was considering dropping PostGIS. Why? Not because it’s not fast enough, but because the organization was having a hard time finding experienced database administrators (DBAs). That led me and others to ask some pointed questions: How hard is it to teach an Oracle DBA how to support PostGIS? Where would potential employees learn to be a PostGIS DBA? Should we teach it in schools?

Articles

Takeaways from the 2014 Location Intelligence Summit

Maurits van der Vlugt is a senior consultant in SKM's spatial business unit. He is responsible for Geo-IT service and OGC solutions delivery throughout Australasia. He provides strategic and technical consulting services with a focus on systems integration, web services delivery, distributed systems and spatial data standards. Recently both authors have been working to improve the user focus of web mapping tools.

Directions Magazine

Segmentation systems represent gathering individual objects such as customers (customer segmentation), markets (market segmentation) or neighborhood (geodemographic segmentation) into groups called segments.A segmentation system is created through the process of clustering, also known as cluster analysis, where similar objects are grouped into homogenous clusters (segments).These clusters should be as different from each other as possible.

Segmentation systems provide synthetic information about objects by focusing on differences in demography, lifestyles, consumer behavior, etc.Geodemographic segmentation systems are usually based on such variables as age, housing, family status, income, education, ethnicity, mobility, labor force, religion and other census variables.Such systems assume rather homogenous characteristics for all customers within a given geographical neighborhood.Lifestyles segmentation systems include additional data derived from various surveys on preferences and lifestyle behavior, related to autos, journals/magazines, TV, leisure, food and drinks, credit cards, etc.Customer segmentation systems refer more to individual customers than to geographic neighborhoods and are usually based on proprietary survey data about customers.

All segmentation systems are created using a similar methodology.They are based on a large number of input variables coming from various data sources.Variables in a final list are known as the "diagnostic" ones.Basic steps used in creating segmentation systems include: 

4. Agglomerating objects (customers, markets, geographic neighborhood) into segments

5. Providing summary information about each segment, naming segments and determining which object is the most typical for each segment

6. Mapping segments and linking them to other data sets.

Statistical software packages such as SPSS, SAS, S-PLUS or data mining software such as Clementine are required to perform some of the steps listed above, especially to reduce the number of variables, measure similarity between objects, and agglomerate them into segments.

There are many commercially available segmentation systems. They have been created in many countries for different levels of geographic aggregation and using a variety of data sets.The first segmentation systems were created in early 1970's.Emerging GIS technology caused the dominance of a geographic aspect in such systems.Recent trends in segmentation systems focus more on specific target groups (micromarketing).

Examples of the most common commercial segmentation systems available today are: MOSAIC (available for more than 20 countries and targeting more than 800 million customers), PRIZM, PSYTE, ACORN and many others. These systems were created by companies with experts from marketing, demography, geography, statistical analysis and data mining.Commercial segmentation systems are often based on data sets not publicly available.They represent the state of the art and are updated regularly; usually once a year or even every six months.

However, they have some serious drawbacks.First of all, these systems are expensive, costing thousands of dollars.For some small businesses this factor can be a real constraint.Second, all of them are general purpose systems, not oriented toward individual businesses or particular smaller geographic neighborhoods.These systems can serve any type of business located within any neighborhood, but business owners might be particularly interested in using specific type of input variables, including their own databases about customers.So in some cases, it might make more sense to create a customized segmentation system.Customized systems are not just low budget alternatives to commercial segmentation systems.Both types of systems can complement each other.

Access to a statistical package, some expertise in using statistical methods and a GIS are necessary for creating a customized segmentation system.These obstacles can be overcome by outsourcing some tasks to professional consultants.

Diagnostic variables and their properties

Input variables should be relevant to the given type of business, they should be available at the desired geographic level of aggregation and additionally, they should have some other critical properties. For example, for a system customized to banking services, such potential diagnostic variables could include: daytime population, disposable and discretionary income, distance from the centroid of a given geographic polygon to the closest bank or banking machine expressed in driving time, consumer spending potential on such categories as government retirement and pension funds, income tax, tax refunds, employment insurance premiums, gifts to charitable organizations, purchase of automobiles and trucks, property taxes, mortgage interest, etc.

In addition to being relevant, the diagnostic variables should be available at the desired geographical level of aggregation. All these variables are available in Canada, for example, at the aggregation level of 200-300 households.Commercial segmentation systems exist for census block groups (US PRIZM and US MOSAIC), postal codes (MOSAIC Canada), dissemination/enumeration areas (PSYTE Canada), and enumeration districts (ACORN).There are systems referring to a single street (in Belgium and Luxemburg) or even a single building (in Germany).

Diagnostic variables should be also geographically differentiated and relatively stable in time.For example, the ratio between males and females is usually not highly spatially differentiated and therefore is not a good diagnostic variable.The coefficient of variation can be used as a measure of a geographic differentiation.This coefficient is the ratio of standard deviation to the arithmetic mean.The following table shows arithmetic means and standard deviations for seven diagnostic variables.Figure 1 presents the corresponding coefficients of variation.The best diagnostic variables are the density of population and household income.The least geographically differentiated variables are urbanization and literacy and they would be the least recommended as diagnostic variables.

How many diagnostic variables should be used?

There are thousands of initial input variables used in commercially available segmentation systems.However, the final list of diagnostic variables is much smaller and varies between 50 (PIN system by Pinpoint Analysis Ltd.) and 120 (Super Profiles system by CDMS Ltd.). Usually, census variables constitute from 50% to 100% of the total number of diagnostic variables in these systems.The number of initial and final diagnostic variables in customized segmentation systems should be within a similar range or even smaller than for general purpose systems.

How to reduce the number of diagnostic variables?

The initial list can include hundreds or even thousands of potential input variables.Using too many variables can lead to what is known in statistics as "overfeeding the model," and this situation should be avoided. If two variables are highly (significantly) correlated, their explanatory value is very similar and using both of them is redundant.The following table presents the correlation matrix between every pair of seven diagnostic variables.These coefficients have values from +1 (perfect positive correlation), through 0 (no correlation) to -1 (perfect negative correlation).Very high correlation is marked with ** and high correlation is marked with *.For example, density of population is positively correlated with two variables only, whereas urbanization is highly correlated with every other variable and very highly correlated with five of six variables.This variable (urbanization) can be eliminated and another correlated variable will be used instead.

Analyzing the correlation matrix is a complex and tedious process, especially when dealing with hundreds of variables. Additionally, so-called partial correlation should be analyzed to see how relationships between variables will be changed if one variable is removed.Some other statistical methods such as multiple linear regression or factor analysis / principal component analysis can be also useful for eliminating variables. However, some expertise in using multivariate statistical methods is necessary.Tools needed for these tasks are commonly available in statistical software packages which provide fully automated procedures.

Multiple linear regression analysis can help to select the set of variables (predictors) that are the most suitable for predicting another single variable.However, the major disadvantage of this method is that one variable should be identified as a target variable.The following table was created using the multiple regression analysis.This table shows that four of seven variables were selected for predicting another variable (infant mortality).Variables not selected are: density of population, urbanization level and household income.Should they be eliminated? Maybe, if the target variable (infant mortality) is really a critical for a given segmentation system.

Principal component analysis replaces individual variables with components that are common underlying dimensions for these variables.The following table shows how the principal component analysis can help in eliminating variables.In this table, the rows are variables, columns are components and the table entries are coefficients of correlation between variables and components.For example, the first component can be used instead of four variables: population increase, birth rate, literacy, and household income.The second component can replace two other variables: death rate and urbanization.The third component corresponds just to one variable.

Components 1 and 2 count together for 70% of the total variance explained (see the table below).That means that instead of six variables, two components can be used as diagnostic variables and the number of input variables will be reduced.

How many segments should be created?

When the final set of diagnostic variables is ready, the number of segments should be determined.The number of segments in commercial systems varies from 25 (PIN system in UK) to more than 150 (MOSAIC Canada, Super Profiles UK).In addition, segments are often agglomerated into groups.Various commercially available systems have from 10 to 40 groups of segments.Sturge's rule can be used as a rule of thumb for determining the recommended number of segments:

Number of segments = 1 + 3.3 log (number of objects)

For example, for 1,500 geographical units, the number of segments should be between 11 and 12:

1 + 3.3 log (1,500) = 1 + 3.3(3.18) = 1 + 10.5 = 11.5

Statistical and data mining software packages provide some common clustering algorithms such as hierarchical clustering, K-means clustering, two-step clustering or Kohonen networks.In hierarchical clustering, for example, the first step is to determine similarity between objects.Some similarity measures can be applied even to categorical (not numerical) data.As a result, a proximity matrix can be determined showing how similar any two objects are.However, the size of such a matrix often makes it impossible to display it.For example, there are more than 1.5 million objects included in the UK MOSAIC system and the proximity matrix would have the size of 1.5 million times 1.5 million entries!

The agglomeration process creates segments from similar objects and can be done in many ways.Commonly used statistical software packages provide numerous (5-10) agglomeration methods.The alternative approach to hierarchical clustering is the K-means clustering algorithm.In K-means clustering, the proximity matrix is not created, similarities between objects are not determined, and objects are not agglomerated based on these similarities.Instead, the algorithm reassigns objects to segments, and some objects can change their assignment from one segment to another.Each object is assigned to the segment for which its distance to the cluster mean is minimal.It is recommended that the number of objects per segment should be similar.Otherwise, some unique segments will contain just a few objects or even only one object.Similar procedures that are used for creating segments are also used for creating groups of segments.

The byproduct from K-means clustering is the distance from each object to the centroid of each segment.This distance can be used to determine the most typical object, such as a census block group, enumeration district, postal code, etc.This object can be identified on a map, for example, by its ZIP or postal code, corresponding images can be obtained and used to illustrate the nature of this segment, etc.The following table presents the portion of the output data sorted first by the segment ID and then by the distance to the segment centroid.For example, there are fifteen objects belonging to segment #5.The minimal distance to the centroid of this segment is 128 and the most typical object for this segment has ID 25.

Describing segments

A concise description should be provided for every segment based on such statistics of diagnostic variables as arithmetic mean, minimum, maximum, standard deviation and count.These statistics can be obtained using not only statistical software packages but also spreadsheets, GISs, etc.These tools perform such operations as "group by..." or "agglomerate by..."

OLAP cubes, also known as pivot tables (in spreadsheets), represent the most suitable tool for such a task.The following table contains basic statistics for seven hypothetical diagnostic variables and for seven segments. Maximum values in this table are marked using red color, whereas the minimum values are marked in blue.Based on the information from this table, segment #4 can be characterized as:

...having the highest mean values for the density of population (the arithmetic mean is 4975.0, no value is less than 4456.0 and the maximum density reaches 5494.0) and the level of urbanization (the arithmetic mean is 97.0, with no value is less than 94.0 and the maximum reaching 100.0).This segment can also be characterized as having both the lowest birth rate (14.5) and death rate (6.0) per 1000 people.In addition, this segment has the most differentiated values of the density of population (standard deviation of 734.0) and the most homogenous values for the urbanization (standard deviation of 4.2), birth rate (2.1) and death rate (0.0).The last number indicates that all objects belonging to this segment have identical death rate (6.0)...

Other segments can be described in a similar way using information provided in the summary table.Segment #7 does not have any extreme characteristics. However, this segment can be described by comparing its values versus national averages provided in the column Total.

 Beside these characteristics, some additional geographic information can be provided. For example, the short description of the Asian MOSAIC segment (one of sixty segments from the Canadian PSYTE segmentation system) is:

"Inner city areas in which Chinese and other Asians are concentrated.In Toronto and Vancouver these areas are known as "Chinatown." Dwellings are older, a mixture of owned, single detached, rented semis and low-rises.Many families have children and typically these are teenagers often at university."

This description was created using census variables such as population by ethnic origin, dwelling characteristics, family type, age structure and education.In addition, geographic variables were used referring to the location of this segment across Canada (Toronto, Vancouver) and within urban areas (inner city areas).

Giving names to segments, after describing each of them, is a relatively easy task.In fact, this can involve some fun as well, since segment names are more informal than informative.The following are examples of names from the UK MOSAIC system: Family in the Sky, Mid Rose Overspill, Rejuvenated Terraces, or Graffited Ghettos.

Mapping customized segmentation systems is not a difficult task.A GIS can be an asset. Basically, two data sets are required for mapping: the boundary file and attribute table.The boundary file should contain polygons representing geographical aggregation units such as counties, census block groups, enumeration areas, enumeration districts, dissemination areas, census divisions, etc.The attribute table should contain at least two columns: the ID for every geographical unit and the segment ID.When these two tables are linked, and segments are mapped, the map legend should contain segment name and its short description rather than an ID.The newly created segmentation system can be linked to other data sets through the key item that is usually the ID for geographic units.The map below presents the portion of a student assignment on creating customized segmentation systems for the Statistical Methods course, GIS for Business Advanced Diploma program at the Centre of Geographic Sciences in Lawrencetown, Nova Scotia, Canada.

I will be very pleased to respond to any question regarding problems you might be having with creating your own customized segmentation systems.

A Primer on How to Create a Customized Segmentation System

In the fall of 2010, the Douglas County, NE GIS Department (DCGIS) managed numerous servers in multiple locations throughout the county. The internal IT group did not yet offer a virtualized environment (i.e. multiple, configurable, disposable servers sharing physical resources with other servers), so DCGIS faced the purchase and setup of new server hardware. At the same time, Esri began to push the potential of running its GIS server software in the Amazon cloud. After reading up on Amazon Web Services (AWS) and how to deploy Esri’s software in the cloud, DCGIS officials made the decision to give Amazon a try.

We began by creating an Amazon account and registering a credit card for charges (it now offers a monthly invoicing option). We launched a new server instance using a pre-configured Esri AMI (Amazon machine image). This AMI included the ArcGIS Server and ArcGIS Desktop software pre-installed and configured. Amazon’s virtual server service is called EC2 (Elastic Compute Cloud). Once we launched and connected to our new virtual server, we registered the ArcGIS software with our developer license and we were up and running. Our first task was to copy an HTML file to the Web server and connect to it from the Web. That worked well and the Web page seemed fast. We then transferred some GIS data to the cloud server after installing Dropbox. We published some map services and consumed them with our desktop software and using ArcGIS Online. This worked well. Our next testing goal was to port one of our smaller FLEX applications to the new Web server and reconfigure it to consume map services running on that instance. This test also went off without a hitch.

Based on this early testing success, we wanted to expand our efforts to include a separate database server instance and install a third-party application, Cityworks, onto the Web server. Cityworks Server AWS is a GIS-centric, browser-based work and asset management system. These initiatives took us into 2011. By late winter 2011, we had two Amazon server instances running – a Web server and a geodatabase server. We also had Cityworks operational and running on top of ArcGIS Server in an Amazon cloud instance. We began moving some of our production applications and map services out to the cloud. In spring 2011, we deployed our Pictometry Online software to an Amazon EC2 cloud instance running MySQL. By summer 2011, we had 75% of our enterprise GIS operations running on Amazon cloud servers.

Today, we continue to create and modify geospatial data locally. We replicate these data to our cloud servers and run (almost) all of our Web services and applications in the cloud. Even though we believe the AWS platform is an affordable alternative to hosting your own servers, we are always seeking ways to drive down our costs. Recently, we have been tinkering with Amazon’s S3 storage platform for storing data and hosting Javascript Web applications. This platform offers reduced rates for data storage and provides increased performance. We are also utilizing scripting to automatically stop and restart non-critical servers that host “business hours” applications for additional cost savings.

From a GIS administrator’s perspective, our number one benefit derived from using the cloud has been flexibility. The flexibility to deploy a new server instance or an image of an existing server and only pay for the time we use it is extremely valuable. We are better prepared to test new versions of software and custom applications. From an organizational point of view, the biggest benefits have been reliability and the reduced dependency on internal IT resources. The costs are predictable and Amazon provides many options for driving those costs down.

Along the way, we have learned a lot of tips and tricks. The process included some hard lessons and plenty of stops–and-restarts. Successfully moving to the cloud includes careful planning, lots of testing, and resilience. We encountered numerous roadblocks, some large and some small. For example, hosting a production database in the cloud means desktop software users within your network need a way to connect. We tried solving this with database connections (ODBC), but found performance lagged. We then tried connecting via Web services but still didn’t get the performance we needed. Wherever we couldn’t convert a desktop user to a Web-based alternative, we eventually set up “micro” server instances on Amazon’s cloud where our user could login remotely and connect to the cloud database – kind of a cloud-based virtual desktop. This solution has proven to be very reliable.

Another consideration is Internet bandwidth. Like many organizations, we have remote sites where users don’t have the Internet performance we do at the main office. This should be considered ahead of time. Can you upgrade the bandwidth? Can you move the staff? Is remote connectivity an option? We have also spent a considerable amount of time on spatial data replication. Reliable data synchronization is critical to a cloud-based GIS solution. The DCGIS staff documents these speed bumps and all of the other tips and tricks learned along the way. Not surprisingly, our advice for success is detailed planning, thorough testing and a willingness to embrace change.

For the Douglas County GIS Department, running our GIS in the cloud has enabled us to become more flexible, reliable and scalable. Our performance has increased and our costs are roughly the same as they were when we purchased hardware ourselves. As we continue to expand our service and data solutions, the cloud is enabling us to work more efficiently and effectively.

What Douglas County Learned Moving its GIS to the Cloud

Addition of Schneider Electric customer engagement tools offers better network understanding for optimum energy aggregation

Sept. 9, 2015 – Schneider Electric announced its partnership with IPKeys Technologies, LLC, a provider of standards based energy interoperations software, network communications engineering, and enterprise security solutions that enable energy providers and their customers to achieve a more reliable, safer, and more economically beneficial smart grid.

The combination of IPKeys EISSTM Online technology with Schneider Electric’s cloud-based customer engagement, energy management and demand response system – Energy Profiler Online (EPO) – will give IPKeys Power Partners load aggregation service’s customers the ability to monitor energy performance in near real-time, track energy savings and actively participate in automated demand response events. This capability to monitor accurate, detailed energy usage data means facilities, as well as utilities, will benefit from the most cost-effective and sustainable energy possible.

Utilizing the OpenADR 2.0b protocol, facilities benefit from increased flexibility and improved usability for better customer experience and enhanced performance. Being able to accurately view detailed energy information allows customers to identify cost savings opportunities, achieve sustainability goals, and improve reliability. Additionally, this partnership will help customers enhance their demand response program performance and manage peak demands as they occur.

This standards-based bundled solution is the first customer engagement and demand response solution that provides both near-real time load reporting and real-time load management utilizing OpenADR 2.0b, demonstrating further adoption of the standard to benefit utilities, aggregators, and their customers.

“The opportunity to advance our partnering with Schneider Electric and integrate EPO with our EISSTM Online platform presents new advantages for consumers of electricity,” said Robert Nawy, Managing Director and CFO, IPKeys. “Energy managers and a wide range of consumers will now be able to benefit from technology that was originally deployed for utilities.”

EPO provides value to all types of energy services providers, including regulated investor-owned utilities (IOUs), municipal utilities, co-ops, energy retailers, and now demand response aggregators. Schneider Electric’s partnership with IPKeys Power Partners demonstrates further adoption of the OpenADR standard, increasing flexibility and improved usability for a better customer experience and increased performance across the energy value chain.

As a global specialist in energy management with operations in more than 100 countries, Schneider Electric offers integrated solutions across multiple market segments, including leadership positions in Utilities & Infrastructure, Industries & Machines Manufacturers, Non-residential Building, Data Centres & Networks and in Residential. Focused on making energy safe, reliable, efficient, productive and green, the Group's 150,000 plus employees achieved sales of 31 billion US dollars in 2013, through an active commitment to help individuals and organizations make the most of their energy.

IPKeys Technologies, LLC is an emerging strategic partner in the energy, DoD, commercial and federal sectors delivering expertise in the definition, development, integration, and deployment of secure Internet Protocol (“IP”) technology, software, and communications systems.

IPKeys Power Partners Teams with Schneider Electric to Provide Enhanced Energy Management Capabilities

When we think of GIS professionals, our thoughts often fall into one of two categories: first, the seasoned professionals that have been working with the technology since its infancy —essentially the "Yodas" of geographic information systems, or alternatively, the newly graduated techies behind amazing innovations we couldn’t have dreamed of a decade ago.

We rarely think of high school students — but, believe it or not, there are a number of young, motivated individuals that are not only learning GIS while completing their secondary education, but who are also applying their new skills to help solve today’s most significant problems.

At Washington-Lee High School in Arlington, Virginia, students have the opportunity to participate in an innovative program called Geospatial Semester, a collaboration between Virginia high schools and the Integrated Science and Technology department atJames Madison University. As part of the program, students develop working relationships with professional agencies, assisting them in finding solutions to real-world problems. In 2012, students worked with the U.S. Fish and Wildlife Service to identify wetland areas that fall within government easements. These locations are ideal for wetland conservation experiments, and for developing the plans needed to prepare for changing climate condition.

Several students have gone on to present their work at such prestigious events as the annual ESRI International User’s Conference, and to organizations including the U.S. Geospatial Intelligence Foundation, local school boards and partner organizations.

Created by Dr. Bob Kolvoord, with the help of Ms. Kathryn Keranen, the Geospatial Semester was designed specifically to engage students in their final semesters of high school. The only requirement for the course is to be either a junior or senior; students are not required to be among the school's top performers to participate.

“The Geospatial Semester class appeals to a broad cross-section of students, often ones that haven’t had a lot of academic success. Interestingly, the open-ended nature of the Geospatial Semester draws in students who aren’t engaged by typical high school offerings (and sometimes really challenges students who are very successful in standard high school classes),” Dr. Kolvoord stated in a recent email exchange.

Students speak very highly of the course, often because of the way it differs from a traditional high school class, he explained. In this program, students are given a much greater level of autonomy and responsibility, particularly in their final project; as a result, the course functions as a transition between high school and whatever comes next, and many students continue to utilize their geospatial skills long after the course.

“[Many] students move from the GSS directly into internships, and some have gone to work using their geospatial skills. Most students move on to higher education or the military, and some will follow a geospatial path, while others move into non-geospatial areas, but often use their geospatial skills to augment their work," Dr. Kolvoord wrote.

Students in the program receive college credit at JMU; nearly 3,000 students have earned college credit since the program began in 2005.

In Rochester, other high school students are using GIS to tackle difficult social problems within their communities as part of GIS Scholars

working in collaboration with School Without Walls

The program is designed specifically to teach students how to perform professional, publishable GIS-based analysis, a marketable skill set that that could lead directly to a career in GIS.

Recently, this group of students worked on data analysis and map creation related to street-level information on crime, housing conditions, public transportation routes, schools and minority-owned businesses. Ultimately, this data will help answer important demographic and social questions about the city of Rochester, such as: to what conditions are young children exposed, as they walk every day to and from school? Where is the ideal location for new bus stops or small businesses? To work on these maps, GIS Scholars was awarded a grant of $50,000 by the city. Even more recently, they were subcontracted to work on discovering the locations of high impact corner stores for Finger Lakes Health Systems Agency.

For many students, opportunities like these would be beyond their reach if they had to pay college tuition to attain them. Through these innovative programs, however, they are acquiring the skills they need to move directly into well-paid positions in the geospatial industry, without the high cost and associated debt of a college degree. For those that do continue on to college, they begin their higher education with a considerable advantage, and an early focus on skill application that will serve them well in their future careers. Perhaps even more importantly, these students are also demonstrating that they can, even at their young age, make a significant contribution to solving some of society's most pressing problems.

Innovative geospatial programs turn high schoolers into GIS pros

The final Census 2010 tally will draw a definitive portrait of America’s population in 2010 and won’t be available for some time, but a careful sifting of available population estimates and projections reveals likely results of the 2010 Census. PBBI’s Chief Demographer Tom Exter takes a look at the data that are already available from Census 2010 and discusses some trends he’s seeing emerge.

Tables are available in the accompanying PDF document and include Population by Age in 2010, State of the States in 2010, Major Metro Markets in 2010, Ethnic Diversity in 2010, Living Arrangements in 2010, Bachelor's Degree or Bust in 2010, Workfork Demographics in 2010 and Retirement Roulette in 2010.)

As Census Bureau workers wrap up their massive decennial data collection effort, businesses are eager to know the results. Unfortunately, the numbers won't be released until next year. State total populations, used for reapportioning the House of Representatives, must be reported to President Obama by December 31st this year. But detailed results about the demographic characteristics of America's population and households will take years to fully tabulate. How has the Great Recession affected regional growth? Which cities are most impacted by immigration? As Baby Boomers turn "sixty-something," how are their living arrangements changing? Answers to these questions require not only fresh census results but time-consuming, in-depth analysis.

The final Census 2010 tally will draw a definitive portrait of America's population in 2010. In the meantime, several key trends are already apparent. A careful sifting of available population estimates and projections reveals likely results of the 2010 Census. What follows is an analysis, drawn from current statistics and the best-available demographic projections, that previews those key trends. Business planners can act today, knowing this tentative but reasonable sketch of the changing consumer landscape.

The 2010 Census will show that the nation's population grew to 309 million, up 10 percent from 281 million in 2000. That's 28 million more residents of these United States. About one-third of that growth (over 9 million) was due to net immigration, while two-thirds was due to natural increase or births minus deaths. Median age increased from 35.3 years to 36.8 years, a clear sign that the population is aging. But the impact of aging falls differentially on the nation's generations. Baby Boomers (aged 46 to 64 in 2010 - easy to remember because they were born between 1946 and 1964) began turning 60 at mid-decade, and now are storming the age 65 threshold, in a demographic tsunami. Over the decade, the age group 60 to 64 years grew by 55 percent for a gain of nearly six million people. Echo Boomers, largely the children of Baby Boomers, became the latest "twenty-somethings" only to start their careers in the face of the Great Recession. Baby Busters, born between Boom and Echo Boom, are now in their late 30s and early 40s, praying the Boomers will be able to afford retirement so they can move into that corner office.

As population growth plays out across the age spectrum, it also plays out geographically. The 2010 Census will show that Nevada - while still the overall 10-year fastest grower among the states - hit the skids the hardest in the second half. From census to census, Nevada grew by one-third. Breaking that out between the first half and second half of the decade, the state grew by 20 percent between 2000 and 2005, but only 10 percent between 2005 and 2010. Two other high flyers experienced substantial slowdowns: Arizona grew 16.5 percent in the first half and 12 percent in the second half and Florida, 11.3 percent and 4.9 percent, respectively. Still, other growth states picked up the pace from the first to second half of the decade: Utah, up from 11.9 percent to 13.7 percent; Texas, up from 9.4 percent to 10.8 percent; Colorado and North Carolina, up from about 8 percent to 10 percent; and South Carolina and Washington State, up from about 6 percent to 8 percent. Clearly, there are still relative winners and losers in the high-stakes race to rebuild state economies and attract interstate migrants.

Politics, of course, is also in the mix. The 2010 Census will show (according to these PBBI preliminary calculations) a shift of congressional seats from states with a declining share of the national population to those with increasing shares. Reapportionment is the process of allocating the 435 House seats among the states such that each representative represents approximately the same number of people. Each state - not counting the District of Columbia - gets at least one seat so the census results are fed into a formula to reapportion the remaining 385 seats. Soon after official results are posted to the White House by December 31st, we will likely see that Texas is the overall winner with a gain of four seats. The biggest loser will be Ohio with a loss of two seats. Nine other states will lose one seat each, while seven states will gain a seat. There will be an interesting battle for the last seat among five states: two red states (North Carolina and Arizona) will battle two blue states (Minnesota and Oregon). A swing state (Missouri), however, is likely to be the winner according to our calculations. May the state with the highest census form completion rate be the winner!

The stakes are also high for the nation's metropolitan markets. The top five metro growth markets (measured by absolute population gain) were: Dallas (up 4 million), Atlanta (up 3 million), Houston (up 2 million), Phoenix (up 1 million), and Riverside (up almost a million - 938,578). The top five fastest growers (measured by percent increase in population) among metros with at least one million population were: Raleigh-Cary, NC (up 45.7 percent), Austin-Round Rock, TX (up 40.5 percent), Las Vegas-Paradise, NV (up 40.0 percent), Phoenix-Mesa-Scottsdale, AZ (up 36.6 percent), and Charlotte-Gastonia-Concord, NC-SC (up 34.1 percent). But those are the 2000 to 2010 figures. The metros that "put on the breaks" the hardest (whose growth rates fell the most in the second half of the decade) were: Las Vegas (down 11.5 percentage points), Riverside-San Bernardino (down 10 percentage points), Orlando (down 9.5 percentage points), Phoenix-Mesa-Scottsdale (down 5.1 percentage points), and Atlanta-Sandy Springs-Marietta (down 4 percentage points). The lesson here is not "the bigger they are the harder they fall." The lesson is: Population growth trends in the nation's largest metros closely track local economic trends. Similar indicators can be found in smaller metros, the micropolitan areas tracked in the Census Bureau's population estimates.

Many metro markets are known for their ethnic or cultural markets. But the big picture is this: The 2010 Census will show that the Hispanic population grew to nearly 50 million, up 40 percent (or 14 million) in 10 years. So in fact, Hispanic population growth accounted for half of total U.S. population growth during the decade. If the nation grew by 10 percent, the Hispanic population grew by four times that - 40 percent. The Asian-Hawaiian-Pacific Islander population grew by 33 percent (more than three times the national growth rate), and the African American population grew by 10 percent, equal to the national growth rate. When the final results of Census 2010 are tallied, some analysts will look for "minority-majority" states, metros and counties. But most businesses will look for growth wherever they can find it. Increasingly, that will be in communities where both internal and international migration trends over the decade created more ethnically diverse markets.

The 2010 Census will show that changing living arrangements, formally known as "household composition," remain a strong indicator of purchasing behavior. Married couples with children under age 18, the storied target market of the last half century, now represent only 22 percent of American households. Twenty-seven percent of households are empty-nest married couples; many are Baby Boomers, whose children have gone on to bigger and better things. The two other dominant household types are the 28 percent of households in which people live alone, and the 12 percent of households containing single parents, primarily female-headed but also male householders caring for children. So, the 2010 Census will confirm that American's living arrangements are also increasingly diverse - reflecting values, life stage, life style and the meanderings of young adults.

Some 2010 Census results can be estimated through analysis of the American Community Survey which replaced the traditional long-form census questionnaire. While ACS results will best apply to higher level trend analysis, there will be plenty to work with given the annual release of one-year, three-year, and five-year data. In the meantime, here's a sampling of what the ACS results for 2010 will show.

The 2010 Census will show that women overall, but especially younger generations of women made the most gains in educational attainment. Within the cohort of young adults aged 25 to 34, fully 33 percent of women but only 26 percent of men have attained a four-year college degree or higher. Women doubled that gap from 3.5 percentage points to 7 percentage points between 2000 and 2010. Among 35- to 44-year-olds, women have more higher education degrees than men by 32 percent to 29 percent. Overall among men and women aged 25 years and older, the education gap is now negligible but younger women clearly have the momentum. Trends in educational attainment ultimately play out in the labor force, which in turn drives trends in individual earnings and household income.

The 2010 Census will show that while overall aging of the work force is the dominant trend, women aged 55 to 64 (those leading-edge Baby Boomers) are increasing their contribution more than men. Their labor force participation rate grew by 8 points compared to a 2 percentage point increase among men in that age group. The story of the last half century was the increasing labor force participation of women and the earlier and earlier retirement of men. Now that defined pensions are fading into the history books, replaced by the 401K and stock market volatility (to say the least), both men and women - especially those Baby Boomers approaching retirement age - are working more and working longer. Of course, the current unemployment crisis has hit this age cohort especially hard, and it has hit men - 10 percent unemployed in April 2010 - a bit more than women - 8 percent unemployed in April 2010.

The end game is retirement roulette. The 2010 Census will show that household incomes have remained roughly the same in 2010, controlling for inflation, as they were in 2000. If one trend can be deduced it's that the incomes of mid-life householders and pre-retirees have gained a few points, primarily due to multiple household earners working longer hours and postponing retirement. Retirees have benefitted from inflation-adjustments to Social Security. Younger workers, on the other hand, have seen average incomes remain stable or decline. Many, of course, have stayed in school longer investing in their education. But alas, the decennial census - or even the old Current Population Survey, the official Census Bureau instrument for measuring income and poverty - is not the best way to measure income. Snapshot income measures tell us nothing about an individual's or family's historical income trajectory, to say nothing of accumulated wealth. But sadly, the block group level income distribution by age of householder will not be produced by the 2010 Census. The ACS will produce useful higher level income statistics, including age by income, but new sources and modeling methodologies must be developed to better track income trends.

Every census provides businesses with a needed data refresh. The 2010 Census will be no different. New benchmarks and growth patterns will inform site location research and marketing efforts well into the future. But clearly, the time is now to begin to anticipate census results and to plan for unprecedented access to mountains of new data. So much data and so little time to really enjoy it!

What the 2010 Census Will Show

Manifold®
System Release 5.00 - Professional Version

The installation for
Manifold System consists of a three step process. I would recommend users
print out the readme files for each step before the installation. Each
step is listed under a Setup directory on the Manifold CD. In the first
step, you go to the directory of your operating system and load updated
Microsoft drivers for your operating system such as Microsoft Data Access
Components and Jet. The second step of the installation loads Microsoft
Internet Explorer 6 and a security fix for Internet Explorer 6. Manifold
recommends that users go to Microsofts website and check for any further
updates. The last step loads the Manifold System onto your computer. There
is a Manifold Service Pack 3 that is available on their website where users
can also register and activate Manifold online.

Manifold includes a
Microsoft Word document file on the installation CD. The documentation
is 2,447 pages long and includes many images. The size of the file and
the number of images takes a long time for MS Word to completely load the
file which makes the scrolling through the file very slow until the file
is completely loaded. Manifold suggests that users spend at least a day
reading the Introduction section and working through examples in the Examples
section in the documentation. Manifolds documentation introduces GIS users
to many new methods, user interfaces, terminology and concepts that are
not available in traditional GIS software. The Manifold documentation includes
a very thorough listing of free national and international data sources
available on the Internet.

Manifolds main documents
are projects that are stored with a .map extension. Projects contain a
portfolio of components such as images, drawings, tables, labels, charts,
maps, etc. Comments, queries and scripts are called text components because
they contain text. Figure 1 is a screen shot that shows the Manifold System
GIS. There is a Map component pane, Drawing component pane, Table component
pane and the Project pane is shown on the right.

The Project pane shows the components that are in the current project. When importing a component
into a project, the entire contents of that component are stored in a special
section of the project file. Manifold can import data from over 70 different
vector, raster, terrain elevation and database formats. One thing to note
about importing data, Manifold always goes back to a default location on
the hard drive when you choose to import, as opposed to going back to the
directory from the previous import. Once a component is imported into Manifold,
it is now in the project and has no tie to the original format or file
from which it originated. Therefore, a user can edit the contents of the
original data in Manifold without changing the source data. Manifolds
reasoning for this is the price of hard drive space has come down in recent
years that we can afford to store larger map files since everyone has larger
hard drives now.

To view an individual
component, the user double-clicks on the component which opens a new pane.
You can also view the attribute information for a drawing by clicking on
the plus (+) sign next to a drawing and will list the attribute table under
the drawing name. (See Bay_roads Drawing and Bay_roads table in Figure
2)

thumbnail_temp_1768503022465_h659b1.gif

temp_1768503022465_h659b1.gif

Map panes are seen in
a special type of window that has layer tabs at the bottom. Maps are the
main user interface in Manifold because maps show components like drawings,
images, surfaces and labels components in layers. Maps show their contents
in a stack of multiple layers and can apply a variety of effects such as
transparency that work only with layers. Drawings and images are normally
viewed through the map that includes them. Label components are almost
always used within maps. Maps can show their contents using any desired
projection no matter what the native projections might be of their constituent
layers. Maps work faster if the drawings and images they contain as layers
use the same projection that is requested of the map.

Maps are created by
choosing File-Create-Map menu. Manifold adds the map name and an icon to
the Project pane. (See Bay Area in Figure 2) A "map" in the project contains
very little data, since the contents of the map are taken from the drawing
and image components it contains. Users can then drag and drop components
from the Project pane into the Map. The components will appear as separate
tabs along the bottom of the Map window. Users can also open a Layer pane
if there are many layers associated with a given map. You can only add
components to a Map window that have been imported into the Project pane.
The active layer will have a white tab while the inactive layers will appear
in gray. Layers can be turned on and off by double-clicking on the Layers
tab, the Layers name will be grayed out on the tab when it is turned off.
Any changes made to the component in any window will simultaneously appear
in all other windows showing that component.

Selections can be done
on an image or on drawing. When selecting on an image, Manifold will highlight
the pixels on the image that match the selection criteria. Selections can
be saved in the Selection pane for easy referencing later on.Manifold has a complete
set of selection tools. Users simply press the buttons for the selection
they are trying to accomplish. There are three sets of buttons for making
selections:Select Points, Lines, Areas,
Labels, and PixelsSelect Add, Remove, Invert,
Subtract, and IntersectSelect Touch, Shape, Freeform,
Box, Circle, Circle on Center, Ellipse, and Ellipse on CenterThere is a separate
button which Selects None and sets the selection style.

Manifold includes a
powerful database engine, so users can work with drawings that include
many thousands of objects with many database fields per object in the drawing's
table. Every drawing in Manifold has a database table linked to it. 
Manifold can also link tables into a project from external sources.

There are two types
of database tables that may be imported or linked into a Manifold project:

Tables from files and file
types that may be opened directly by Manifold's own database engines and
so require no other database software.   These include .mdb,
.dbf, .udl, .xls, .db, and .html files as well as ascii or text database
files organized as comma separated value (.csv) form.

Tables from data sources
that are provided by external database engines via OLE DB.  Some such
sources may require external database software. OLE DB also includes support
for ODBC.  Manifold will install a default set of OLE DB/ODBC drivers
for popular databases.

Queries can be created
from multiple tables using statements in Structured Query Language (SQL)
to say what the table should contain. ViewBots are a one-line analytic
instruments that are extremely useful for analyzing tables. ViewBots are
used to perform simple statistics, queries, and other functions, including
measurement functions such as the total area of selected objects.

Manifold has a completed
suite of image editing tools. Users can change the brightness/contrast,
hue/saturation, and adjust the RGB settings of images. Users can also select
areas of the image to save as a selection set which can be used to subtract
out areas of the image. For presentation map creations you can use editing
in drawings, create drop shadows with Gaussian blur, and apply a color
gradient to an image. Manifold allows users to georeference images with
tools that use control points to re-scale, reposition and warp an image
to match a known good drawing or image. Users can manual registration to
re-scale or reposition an image that doesnt need wrapping. Once images
are georeferenced, they can be transformed to any geographic projection.

Manifold can import
terrain elevation data from almost any format known and incorporate surfaces
into maps as shaded 2D images showing relief, slope, and aspect or visualized
as realistic 3D terrain views. Surfaces can be colored by the height of
each pixel and the transparency of the surface can be adjusted to allow
other map features, such as roads or rivers, to be displayed in top of
the surface. Users can select out areas of the surfaces to create new components
of certain areas of elevation.

Manifold's GPS Console
provides interactive control over a GPS (Global Positioning System) device
connected to the computer via a serial port.  The GPS Console allows
setting moving map mode or automatically fetching position and other data
from the GPS device.

Manifold includes an
Internet map server that can publish maps to Internet for people to view. 
Manifold IMS can publish maps, drawings, images or surfaces from .map files
to Internet in conjunction with Microsoft's Internet Information Server
(IIS). The .map file will be published as an .asp file when you export
the map file to a web page.

Manifold can be customized
in several ways. For rapid scripting in Manifold, they recommend users
program with Jscript or VBScript within the software. For more extensive
applications development, they recommend Visual Basic or Visual C++ within
Visual Studio.

Manifold comes with
a lot of functionality built in for a reasonable price of $245/license.
This review took more time than I expected because of the amount of functionality
in the product. I would have like to have had more time for a more thorough
review of the product. Manifold suggests users spend at least a day to
read the introduction and perform the exercises, I would recommend
being prepared to spend a lot more time than that to become fully familiar
with all the functionality. Manifold also suggests that "serious" users
read the entire documentation which could take a great deal of time. There
are a lot of processes and terminology differences in Manifold System GIS
that experienced GIS people wont be familiar with.

For more information about Manifold System GIS, I would suggest looking at the Manifold discussion
forum being hosted on directionsmag.com HERE.

Manifold® System Release 5.00 - Professional Version

Esri, the global leader in smart mapping, has published Map Use: Reading, Analysis, Interpretation, eighth edition.

Esri president Jack Dangermond called the 650-page textbook "a comprehensive primer for using maps effectively."

teaches map fundamentalsâ€”what maps are; the types available; what they portray; the purposes they serve; and how to read, analyze, and interpret them. A chapter on map design basics was added to deepen readers' understanding of what goes into making a well-crafted map. "If you understand how a map is made, you will better understand how to use it," said

coauthor Aileen R. Buckley, a research cartographer at Esri.

While written mainly for students enrolled in college-level cartography or map use courses, the book also serves as an excellent resource for cartographers and professionals who work with geographic information system (GIS) technology, remote sensing, and imagery. People who use maps for scientific research, navigation, and recreation will also find

"A lot of people work with maps every day, but they don't necessarily know how to use them in as rich a way that they can be used," Buckley said. "Reading a map is probably the most common use of maps. You look at a map, you read the content off it, and you learn something new. But maps can also be analyzed. You can make measurements and calculate statistics using information that's on maps."

Today, that analysis is often done using GIS. "While the focus in past editions was on using paper maps to do analysis, this edition focuses on mapping information using digital analysis, primarily through GIS," Buckley said.

Illustrated with nearly 600 full-color maps, photographs, and graphics,

was updated to reflect the latest mapping technology, techniques, and terminology. It is divided into three major sections: Map reading, map analysis, and map interpretation. Chapters cover topics such as the following:

Quantitative and qualitative thematic maps

Interpretation of the lithosphere, atmosphere, biosphere, and human landscape

The new chapter on map design basics explains how to define the map's audience and theme, craft a clear and concise message, choose the right map projection, use proper symbolization, make the map legible, use color effectively, and design maps for the web.

"I think readers will appreciate the chapter on map design, replete with illustrations of both well- and poorly designed maps, intentionally created to show the difference between good and poor design," said

GIS File Library

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