Here's Tennessee's GIS Portal which gives Lat/Long, assessment, owner - etc.

http://tnmap.state.tn.us/assessment/map.aspx?GISLink=002079H+A+01300

And don't forget zillow.com for information on housing prices and information. 
http://www.zillow.com

A co-worker of mine was having issues with her fairly-new Samsung cell phone from AT&T.  The durfwad at the store couldn't fix it, so she was calling AT&T to get a replacement, as she was told by the store to do.  Anyway, she had quite an ordeal with AT&T before they finally decided to send her a replacement cell phone.  I won't be going with AT&T for cell service anytime soon after hearing about this.  The co-worker was trying to troubleshoot her phone and was turning it off and then back on.  I was disappointed by the boot-up time of her cell phone.  As time has gone by and phones have become more advanced, they have been taking longer and longer to boot.  I'm old school, I still say "boot."

That got me thinking.  Her phone took a good 20-30 seconds to start up.  It reminded me of the Motorola Razr V3m that I have from work.  The Razr is a clunky phone.  The buttons are just bumpy labels on a continuous surface, which makes it easy to fat-finger the buttons.  The Razr also insists on making noise on startup and shutdown unless you put the phone in vibrate mode or hold down the power button while shutting down.  But here's my biggest beef about the Razr.  The thing takes forever to boot up.

I decided to do a comparison of phone boot-up times.  Motorola Razr V3m versus Kyocera KX1 (aka SoHo).  The Kyocera is my personal cell phone.  This was by no means a scientific comparison.  I started the phones simultaneously and quickly started my stopwatch.  I considered the phone booted-up as soon as the screen quit going through conniptions and was in its standby/ready-to-make-a-call mode.  The Kyocera took approximately 15.6 seconds to start.  The Motorola slogged in at approximately 27.8 seconds.

Am I just impatient?  My Kyocera is acceptable; I wouldn't want to wait much longer than 15 seconds between when I turn the phone on and when I can make a call.  I don't see why it should take a phone more than 15 seconds to start.  I mean, what does it have to do?  Light up the LCD, send power to the antenna, find a tower (not hard to do anymore), check in with the tower, and put up the user interface.  Maybe I have vastly oversimplified what goes on.  The Razr mocks you for several seconds with the Verizon logo, then shows some silly animation about VCast, insists on showing me my own phone number, then is finally ready to make a call.  The Kyocera shows an hour glass for about 10 seconds, then shows the US Cellular logo for a couple seconds, then shows "Hello" and the KX1 logo for a couple more.

Does anyone else think cell phones have become overly complicated and actually less usable over time?  I'm a techy, but I only use my phone as a phone.  No web browsing, GPS, texting, or photography.  There are better devices for those things.

Book Shopping

I decided to ditch ArcSDE and ArcIMS at the office.  We haven't gone through with our plans to set up our own ArcIMS map online and there hasn't been any clamoring by potential users, so I decided not to recommend maintaining our license.  Instead, what I want to do is make our own Google Map mashup type of thing using the Google Maps API.  I don't know squat about website maintenance, configuration, or design.  I know a little about HTML, but there seems to be a lot of Javascript associated with the Google Maps API.  I spent quite a while looking at some of Google's sites about the API and still have more reading to do.  I have yet to find any good easy-to-follow instructions about how to use the API, or even just a generic description of the process.  I knew there were some books about using the Google Maps API and looked them up on Amazon.  I was specifically interested in "Beginning Google Maps Applications with PHP and Ajax: From Novice to Professional" and "Google Maps Hacks."  They would have cost $42.88 from Amazon with free shipping.  For the record, I looked at some of the used copies on Amazon, but I only would've saved a couple of bucks because of shipping costs.  You don't get free shipping unless you go through the "real Amazon," not a partner.  I then checked Barnes & Noble, since Discover Card gives me a 10% cashback bonus if I go through Discover's website to get to B&N.  B&N wanted a whopping $65 for the same books.  Even with the cashback bonus, it wasn't worth it.  Overstock was also part of Discover's cashback bonus online retailing program, offering a 5% bonus.  They wanted $43.69 for the books, including the $2.95 shipping.  The 5% bonus made them barely cheaper than Amazon.  I then remembered to Froogle the books.  I found that Buy.com had my books, and for an even better price.  I ended up paying a total of $39.58 and used Buy's free shipping.

This reminded me of my college days.  I'd always try to find out the book requirements for my classes ASAP, often weeks before class.  I'd go to the campus bookstore, which always had the list first.  I'd take note of the ISBNs and prices, then would go to the off-campus store, which was usually cheaper and had a better selection of used books.  I'd note the prices there, then go back on campus to a computer lab to shop online.  Amazon, Wal-Mart, B&N, eCampus, eFollett, VarsityBooks, and so on.  I never checked Half.com, for whatever reason.  I'd get a big old spreadsheet going that listed all the books and all the retailers.  I'd then highlight the best deals and buy the books accordingly.  It took some time, but the money I saved (at least a couple hundred dollars during the 6 years) was worth it.

Here's the video of boyish Loopt CEO Sam Altman, at Apple’s WWDC 2008 in San Francisco last month, previewing their new iPhone app using location-based services displayed in Microsoft's Virtual Earth, allowing a blend of social networks "so you can see where your friends are," and integrating friends' journals, current status, pictures they've added, etc.   The blog "WebWare: Cool Web 2.0 Apps for Everyone" and CNet covered his demo as well. 

Easy to imagine government first-responders, social workers, healthcare personnel etc. all making use of these integrated technologies.  Now I'm trying out Loopt on Facebook - very cool. 

Update:  A reader emailed after the above, to ask what I think of Apple; I love Apple, always have.  And I mean always.  With my brother I started messing with a county library mainframe from a Vax terminal as a teenager, and used IBM PCs in college at UVA, and a Kaypro 10 as a research assistant for Prof. Larry Sabato - wow, a 10mb hard disk in a so-called portable behemoth!  But a girlfriend owned an Apple IIc, which is where I first played at amateurish programming, and when I got to Stanford in 1984, the library had been outfitted with brand-new Macs, and I took liberal advantage of them. 

Since then, I've been in heterogeneous environments at work and home, and have owned the following in succession: Macintosh IIcx, Macintosh Performa 6400, PowerBook 1400c, Power Mac G4, iMac (original) and iMac G5 (Intel) - that last one is now used as a networked always-on webcam and screensaver art display at home.  I still own two iPods, gotta get that Zune :-) 

All along I've also used PCs, and the first computer I ever purchased with my own money was a PC-XT clone in 1985, with a 20-megabyte hard-disk.  I thought I'd never fill it...

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Off the top of my head I can think of two. No more will that missing edition of Ulysses remain lost; for if the configuration of the device can be established for a particular missing item, then perhaps it can be found regardless of its location and borrower, since the item is of prime importance. Obviously, the cost to configure items would play a huge factor in determining which items in a library would be marked, but think about the potential for keeping tabs on items in a rare book collection? The potential may be RFID on a global basis.

A second thought is that this might well be 'Twitter in the sky'. Track the progress of the mobile library as it makes its route through the city, suburbs, or urban landscape delivering materials to displaced or immobile patrons. Send an instant positioning message to patrons who want to track their items as they are delivered to their nearest local branch.

Obviously, patron privacy would be a concern, but does the potential for finding lost materials with GPS mean that the delinquent patron will always be found with those materials? Would it ultimately reduce the extra cost of replacement copies by locating lost copies with greater ease?

Metadata often includes:

• Elevation data, either in raster or vector form (e.g., contours)
• Shape layers, usually expressed as line drawings, for streets, postal zone boundaries, etc.
• Coordinate system descriptions.
• One or more datums describing the precise shape of the Earth assumed by the coordinates.

Popular GIS file formats

Raster formats

• ADRG - National Imagery and Mapping Agency (NIMA)'s ARC Digitized Raster Graphics
• BIL - Band Interleaved by Line (image format linked with satellite derived imagery)
• CADRG - National Imagery and Mapping Agency (NIMA)'s Compressed ARC Digitised Raster Graphics (nominal compression of 55:1 over ADRG)
• CIB - National Imagery and Mapping Agency (NIMA)'s Controlled Image Base (type of Raster Product Format)
• Digital raster graphic (DRG) - digital scan of a paper USGS topographic map
• ECW - Enhanced Compressed Wavelet (from ERMapper). A compressed wavelet format, often lossy.
• ESRI grid - binary and ASCII raster formats used by ESRI
• GeoTIFF - TIFF variant enriched with GIS relevant metadata
• IMG - ERDAS IMAGINE image file format
• MrSID - Multi-Resolution Seamless Image Database (by Lizardtech). A compressed wavelet format, often lossy.
• JPEG2000 - Open-source raster format. A compressed format, allows both lossy and lossless compression.

Vector formats

• Geography Markup Language (GML) - XML based open standard (by OpenGIS) for GIS data exchange
• DXF - Contour elevation plots in AutoCAD DXF format
• Shapefile - ESRI's open, hybrid vector data format using SHP, SHX and DBF files
• Simple Features - Open Geospatial Consortium specification for vector data
• MapInfo TAB format - MapInfo's vector data format using TAB, DAT, ID and MAP files
• National Transfer Format (NTF) - National Transfer Format (mostly used by the UK Ordnance Survey)
• TIGER - Topologically Integrated Geographic Encoding and Referencing
• Cartesian coordinate system (XYZ) - Simple point cloud
• Vector Product Format - National Imagery and Mapping Agency (NIMA)'s format of vectored data for large geographic databases.
• GeoMedia - Intergraph's Microsoft Access based format for spatial vector storage.
• ISFC - Intergraph's Microstation based CAD solution attaching vector elements to a relational Microsoft Access database
• Personal Geodatabase - ESRI's closed, integrated vector data storage strategy using Microsoft's Access MDB format
• File Geodatabase - ESRI's geodatabase format, stored as folders in a file system.
• Coverage - ESRI's closed, hybrid vector data storage strategy. Legacy ArcGIS Workstation / ArcInfo format with reduced support in ArcGIS Desktop lineup

Grid formats (for elevation)

• USGS DEM - The USGS' Digital Elevation Model
• DTED - National Imagery and Mapping Agency (NIMA)'s Digital Terrain Elevation Data
• GTOPO30 - Large complete Earth elevation model at 30 arc seconds
• SDTS - The USGS' successor to DEM

Other formats

• Binary Terrain - The Virtual Terrain Project's Binary Terrain format
• Well-known text (WKT) – ASCII spatial projection description (ESRI uses a *.prj extension)
• Well-known binary (WKB) - Binary spatial projection description

Of course, one of the first things I did was look for geo-coded data on wine appellations. Unfortunately, there's nothing there. In fact, I've looked high and low for open-licensed geo data for US wine appellations, with no luck. (Yes, Vestra has this info for sale. But if we buy it, we can't share it, right?) If Mapovino has to code this ourselves, then it's going to set us back a little bit. In the end, our aim would be to then publish the kml / geo-coded data on... GeoCommons, for example.

The appellation data itself is publicly available from several federal sources (here, for example). The challenge is that the appellations are defined in natural language, and according to USGS maps, not latitude / longitude. So there's some translation work that needs to happen. There are a few different ways of taking this descriptive data and turning it into KML files (my first idea involves using the hiking software Topo, but that's another story) - but it will require a human and many hours.

In the end, even emails to the TTB (the wing of the ATF that focuses on non-terrorist related activities) and the USGS have turned up empty handed - they all pointed me to the narrative description files I've found before.

So it looks like we're gonna need an intern and some USGS maps. Know someone who wants to trade drawing skills for some wine? :-)

Technorati Tags: mapovino, af83

The last piece I wanted to cover was working with the layer that I loaded in from a shapefile using ArcCatalog. As I indicated before, I used the PG_GEOMETRY keyword to force the use of PostGIS geometry.

I first left the layer as a simple feature. Within ArcGIS everything behaved as would be expected. I could note no difference in behavior versus what I would expect dealing with any other ArcSDE layer. I was also able to add the layer into uDig, but with a little anomalous behavior.

The layer I was working with was a polygon layer. When I loaded the layer into uDig, it displayed as a polygon layer but the uDig TOC showed a line symbol:

Additionally, the fema_regions layer was not editable in uDig. The other layer editable but had the same display issues. To be fair, I had similar behavior with some layers I had loaded into PostGIS from shapefiles using FWTools. In that case, it didn't happen with all of the layers I loaded. In this case, I have not experimented with enough data sets to determine if the behavior is consistent or intermittent. I would also need to test whether it's the loader, PostGIS or uDig. Time has not been a friend here.

Next, I did some experimentation with versioning. I versioned my milbases layer, created a test version, added a feature and save the edit. As expected, when I connected in uDig, it connected to the DEFAULT version and I did not see my edit. Since this layer was editable in uDig, I added a feature here and refreshed my ArcMap display. In this case, I was able to see the uDig-created feature (in DEFAULT) in both versions (again, as expected).

My previous posts have brought a few questions about raster catalogs and the like. I have been able to load raster data and create raster catalogs. This is not, however, raster data in PostGIS. This is ArcSDE raster data next to PostGIS. PostGIS has no idea the raster data is there (although the raster footprints display in uDig as rectangles).

So, depending on how you implement it, ArcSDE for PostgreSQL can provide interoperability between ArcGIS and an open-source stack. uDig can be leveraged in a pinch to edit data loaded into ArcSDE using the PostGIS geometry type, allowing you to expand to meet demand. Additionally, ESRI has implemented the full geodatabase capability within PostgreSQL, providing a great alternative to shops that was GIS but not necessarily Oracle/DB2/SQL Server.

In future posts, I examine different technology mixes that can be used to build some compelling hybrid stacks.

Now, where's my 9.3 shipment?

I did this last project in NetBeans 6.1 and was generally pleased with it. I was originally an Eclipse fan but have been less and less happy with it with each release. But my real problem is that I need to get more comfortable with the workflow when working with Java and Java tools. This last round of coding was a little more "cowboy" than I am typcially comfortable with.

This time around, I was working with ArcIMS (ugh), JSP and a couple of custom servlets. I found NetBeans integration of Tomcat to be pretty seamless, yielding a test/debug experience similar to that you get when developing ASP.NET in VS2005 and VS2008. I see hooks to integrate with SCC systems such as CVS and SVN. We're currently using VSS (again, mostly .Net work) but I've been meaning to set up an SVN server. This may give me the little extra push I needed.

Anyways, last night was spent at Hartamas Square with some of the GIS crew, reminiscing old times & talking about the new (rainforest!) and concocting up ways of how to deliver bitchiness to certain haughty people. Was trashed in foos by the n00b, but in return, trashed him in pool so that didn't make me feel so bad in the end. Saw a few familiar faces and by the end of the night was so knackered that I couldn't muster up the energy to watch Entourage.

Woke up the today with a great plan to go to the office early and help Kenneth pack and load but ended up going just in time for lunch. Recycled ALOT of papers -flyers, newspapers, scrap & whatnot and headed home to eat New Zealand Natural that Mommy bought yesterday, got my last hep shot and settled down to watch Entourage. Felt sleepy afterwards but woke up with a call from the n00b regarding rainforest rooms. Couldn't sleep after that so chilled online and heeere I am. Will update once I'm back from dinner with the YARRR girls.

x

配合無線城市基礎建設發展，越來越多應用冒出來～  光想就興奮起來～

http://www.digitimes.com.tw/n/article.asp?id=0000096391_BLG50B4AMY8FE5Z2RG5VP

行動導航應用   美日不同調
營運商踏入LBS也以導航服務為第1步，美、日營運商都已投入多年，近年則漸導入地點服務和即時資訊，強化用戶體驗。KDDI Navi針對手機用戶的使用情境，規劃行人導航EZ Navi Walker和車用導航EZ助手席Navi。美國4大營運商AT&T、Verizon Wireless、Sprint、T-Mobile都已先後推出行動導航服務。
各地營運商依其國情和人們使用情境包裝其導航服務，在美國，行動導航以車用為主，訴求其可以相對較低的價格取得PND的功能，當中即時交通資訊為其重要特色。
Sprint和Verizon Wireless的導航服務可透過專用伺服器取得即時交通資訊，在行車區域發生事故或突發狀況時出現警示。即時交通資訊是行動導航服務的優勢之一，因為人們大多數時間花在重複的路徑上，如前往辦公室或返家，若能取得即時交通資訊則能幫助人們提前反應，節省寶貴的時間。

亞洲地區則因都市化程度高，人口密集，行動導航以行人導航為主。在日漸複雜的都市系統中，近在咫尺的目標或許需經過天橋和隧道等複雜的交通路線才能到達。行人導航透過精細的圖資，不只可規劃出最短路徑，甚至可搭配大眾運輸系統時刻表，準時搭上最適班車，一機在手就可掌握城市交通資訊。有些特殊規劃路徑方式，如雨天時的「屋簷路徑」，更是特別受到歡迎。

一般而言，行動導航服務多為月費制。以KDDI為例，行動導航服務雖不如其主打的影像與音樂服務使用者眾多，但卻擁有高滿意度、低解約率的特徵，且用戶以30歲左右的商務人士為主，為KDDI既有年輕、女性用戶外開拓新客源。
此外，行動導航所帶來的廣告商機更是令相關廠商心動，路徑沿線的商家可透過與服務提供商合作，在地圖平台上刊登廣告，吸引過往人的目光。

複雜的都會系統  挑戰行動導航技術
然而，當GPS應用不斷推至個人隨身應用時，傳統GPS定位方式就出現不小的使用瓶頸。和駕車導航不同，行人導航有絕大部分位於室內及易受遮蔽地區，如都會峽谷(Urban canyons)、運動場或是以玻璃做成的辦公大樓門口。
但GPS終端必須要「見天」，接收至少4顆以上、30秒無間斷的衛星訊號才能順利定位，這些地區的地形、結構或建築材質阻礙GPS訊號傳遞，連帶使得GPS終端難以發揮原有的效果，精確定位。衛星之外的定位方法如使用基地台輔助定位的A-GPS(assisted GPS)會有較好的表現，或是使用室內輔助定位如Wi-Fi。
此外，精確導航也意味著需要更精確的圖資。行動導航以行人應用為主，但行人導航地圖需要更多心力製作，並不只是將車用地圖直接轉進手機裡。與行車導航不同點在於行人導航必須提供更加精確的路徑如人行道、小徑、天橋等，詳實與符合行人使用的PoI(Point of Interests)更是行人導航的重點。
地圖供應Tele Atlas將行人導航地圖視為發展重點之一，在2008年的Mobile World Congress上首度展示其歐洲18個國家117個城市的行人導航地圖。對手Navteq也可望在與諾基亞結合後，由原本的PND、車載導航地圖，轉向手機用地圖開發。
行動導航服務乍看之下只是車用導航功能的移植，但由於符合行人使用情境的連網功能，讓它得以發揮更多可能性，可望為手機製造商、地圖製造商、服務提供者等提供切入LBS的機會，增強手機對消費者吸引力，提升服務營收，然而，相對地如何提高定位精準度以及提高更精確圖資，也將是行動導航的最大挑戰，仰賴軟硬體廠商配合。

• A new Convert Graphics to Features function that allows you to create features by drawing graphics without using the editor.
• The ability to export layers to KML (Google Earth) files is now built into ArcGIS Desktop 9.3.
• A new HTML pop-up tool which allows different HTML content to be viewed on individual features.
• The labeling process can be temporarily paused to increase performance while you assemble your map or do analysis.
• A new Address Inspector tool lets you click on the map and get the address for that location (reverse geocoding).

While these and many of the other advertised enhanced capabilities sound terrific, history has taught us to take it slow when upgrading, as brand spanking new versions tend to come with their fair share of bugs. We've gotten in the habit of waiting until ESRI provides at least the first service pack for any new version of software before installing it - and even then, we tend to install it on a test (non-production) machine until we are sure that it works well enough not to corrupt or damage any of our project files.

I hope we can move through this testing phase quickly, as I'm very anxious to start employing these exciting new features. Check out this "What's New in ArcGIS 9.3 Desktop" flash video.

Zur Bearbeitung verwendeten wir das Programm Quantum GIS.

Grundlagen waren die TK25, eine Erfassungsdatei zum Digitalisieren und der Gebäudeplan der TU Kaiserslautern.

Zunächst wurden auf der Erfassungsdatei (Layer: TU_Kaiserslautern) durch einsetzen von Polygonen die Gebäude (1, 2, 3, 13, 16, 32, 34, 46, 49, 62, 63) digitalisiert,…

…danach nach ihren Fachbereichen attributiert und verschieden eingefärbt, sowie mit der entsprechenden Nummer versehen.

Das Ergebnis:

Winners - Kodak (16.92%), Oce (2.40%), Broadcom (2.05%), Garmin (1.45%)

Biggest losers - SiRF (-21.43%), Boeing (-11.75%), Stewart Information Services (-8.50%)

News

Garmin (1.45%) - Partnered with GyPSii for navigation-based social networking.  Introduced revised Astro unit.

GeoEye (-1.04%) - Coverage initiated by JP Morgan at Overweight.

Google (-3.36%) - Average Joes can now digitize new map features (lakes, streets, etc.) to Google Maps.  Well, I guess I'll be out of a job eventually.  If any schmuck can make a mashup or draw a map now, is there room for professional GIS people?

Microsoft (-2.13%) - Private firms and governments can now upload high-quality aerial photography to Microsoft for use in Virtual Earth.  Given how most governmental agencies are unwilling to share their data, I don't see anyone jumping to give their imagery to Microsoft freely.

Yahoo (-3.00%) - Licensed neighborhood boundary data.

Other - Magellan looking to increase sales in Canada.  My opinion:  Improve product engineering and customer service before trying to sell more stuff.  Adobe Acrobat 9 to have some geospatial capabilities like distance measuring and redlining.  ArcGIS 9.3 will be able to export PDFs that take advantage of this.

This would be a good opportunity to describe, in a little more detail, what my unit's responsibilities include, just to provide some context for future blog posts which will be more GIS-specific. As I mentioned, I manage the GIS Support branch for the Caltrans District Office in Oakland. Our region covers the nine-county San Francisco Bay Area. I am lucky enough to have an amazing staff of technical whiz kids comprised of a variety of classifications including Planners, Engineers, and GIS Analysts, who do all the heavy lifting for me. Organizationally, we are located in the Transportation Planning Division, as historically, GIS use evolved primarily as a Planning tool.

As the sophistication of the GIS software has improved dramatically over the years, so has it's use throughout our District Office. So while our unit is housed within the Planning Division, we actually support and coordinate the use of GIS across all functional units throughout the District Office. On the whole, I estimate that 85% of our time is spent generating maps and maintaining spatial data. The other 15% we spend helping those brave souls already using, or starting to use, GIS on their own (through training, software troubleshooting, user group meetings, etc.). The majority of future blog posts will focus on issues related to map generation and spatial data maintenance.

So for now, have a great weekend!

Topology refers to the spatial relationships between geographic features. It describes the relationships between connecting or
adjacent coverage features. Topological relationships are built from simple elements into complex elements: points
(simplest elements), arcs (sets of connected points), areas (sets of connected arcs), and routes (sets of sections, which are
arcs or portions of arcs).

Topology is useful in GIS because many spatial modeling operations don't require coordinates, only topological information.
For example, to find an optimal path between two points requires a list of the arcs that connect to each other and the cost to
traverse each arc in each direction. Coordinates are only needed for drawing the path after it is calculated.

Components of Topology:

Topology has three basic components:

I. Connectivity (Arc – Node Topology):

o Points along an arc that define its shape are called Vertices.

o Endpoints of the arc are called Nodes.

o Arcs join only at the Nodes.

II. Area Definition / Containment (Polygon – Arc Topology):

o An enclosed polygon has a measurable area.

o Lists of arcs define boundaries and closed areas are maintained.

o Polygons are represented as a series of (x , y) coordinates that connect to define an area.

III. Contiguity:

o Every arc has a direction

o A GIS maintains a list of Polygons on the left and right side of each arc.

o The computer then uses this information to determine which features are next to one another.

Explanation of Topology:

TOPOLOGY & LAYERS (THEMES)

Topology refers to the spatial relationships between geographic features. It describes the relationships between connecting or
adjacent coverage features. Topological relationships are built from simple elements into complex elements: points
(simplest elements), arcs (sets of connected points), areas (sets of connected arcs), and routes (sets of sections, which are
arcs or portions of arcs).

Topology is useful in GIS because many spatial modeling operations don't require coordinates, only topological information.
For example, to find an optimal path between two points requires a list of the arcs that connect to each other and the cost to
traverse each arc in each direction. Coordinates are only needed for drawing the path after it is calculated.

Components of Topology:

Topology has three basic components:

I. Connectivity (Arc – Node Topology):

o Points along an arc that define its shape are called Vertices.

o Endpoints of the arc are called Nodes.

o Arcs join only at the Nodes.

II. Area Definition / Containment (Polygon – Arc Topology):

o An enclosed polygon has a measurable area.

o Lists of arcs define boundaries and closed areas are maintained.

o Polygons are represented as a series of (x , y) coordinates that connect to define an area.

III. Contiguity:

o Every arc has a direction

o A GIS maintains a list of Polygons on the left and right side of each arc.

o The computer then uses this information to determine which features are next to one another.

Explanation of Topology:

yers (Themes):

Maps produced traditionally or by automation involved the separation of data into layers, that each contained different types of
features: rivers, roads, etc. These are then combined to form a map where layers have usually been printed in different colors.
To present all the data collected for a given area might require the production of several maps, as they could not all be printed  together. Equally in GIS, data for an area are divided into layers or themes, divided by type but here for the dual purposes of display and analysis.
Themes

The database can be divided into as many layers as is necessary, where each layer contains one characteristic such as soils,  land use, drainage, etc. The layers 'overlay' each other perfectly as a result of Georeferencing and enabling analysis between layers.
source

My experiences thus far with the beta and the release candidate have been pretty positive so it'll be good to get the final product.

Who's starting the pool on the when the first service pack will be out? ;)

It's always fun in tools such as ArcGIS Explorer and Google Earth to browse around your local area and check out the familiar sites, especially when there is an update. In my case, that means browsing around Southern Maryland. The imagery in build 480 for my area was apparently captured in the fall since corn mazes can be see pretty clearly. Here are the approximate coordinates of a couple:

-76.698488, 38.323529
-76.720778, 38.411094

The second is of particular interest. I took my daughter to that one in Pre-K. I know this because the owners helpfully carved the words "Forrest Hall Farm 2005" into the field. This is all the more interesting since the copyright statement at the corner of the screen would lead you to believe you are looking at 2007 imagery.

This kind of like looking for the errors in movies.

KRIEG, KRIEG, KRIEG, KRIEG, KRIEG....!

AUFGEWACHT UND IN DIE KAMPFBEREITSCHAFT.!!!

Russland erwache.!

China erwache.!

Iran erwache.!

Indien erwache.!

Pakistan erwache.!

Asien erwache.! ...

DER SATANISCHE USA-PAKT WILL DEN ATOMKRIEG, um dem Rest der Welt zu zeigen wer der Herr auf Erden ist und wer die Knechte zu sein haben.

Sie sind in des Wahnsinns Ueberheblichkeit, weil sie sich militaerisch, technisch dem ganzen Rest der Welt gegenueber ueberlegen betrachten. Und der Rest der Welt soll in die Steinzeit zurueckgebombt, zurueckversetzt werden, um als Sklavenvieh genutzt werden zu koennen.

Der USA-Pakt der westlichen Welt lebt nun im berechtigten Angstwahn, dass die oestliche Welt sie nun bald in allen Bereichen ueberholt und so dann zwangslaeufig die Herrschaft uebernimmt. Und das aber will der weisse Mann, der Sohn des Satans, mit allen ihm zur Verfuegung stehenden Mitteln verhindern.

IM SCHATTEN DER OLYMPIADE WOLLEN SIE KRIEG FUEHREN.

Wenn China und die sinnlich verbloedete Welt im Olympiataumel abgelenkt ist, dann kann man doch problemlos den grossen Krieg zuenden, denn die westliche Welt interessiert Chinas Image und/oder Erfolg einen ganz grossen Scheissdreck.

DIE WESTLICHE WELT INTERESSIERT SICH NUR FUER SICH UND IHRE ERFOLGE.

Die westliche Welt ist SATANISCHE VERLOGENE EGOMANIE.

Sie reden vom Frieden, um unbemerkt aufzuruesten und Kriege anzufachen.

Sie reden vom Humanismus, um im Schatten erhabener Worte Voelkermorde, Massaker, Massenvernichtungen und Folter unbehelligt ausfuehren zu koennen.

Sie reden bestaendig vom Uebel des 3. Reiches und begehen dabei selber die noch groesseren Uebeltaten.

Man kocht in Deutschland immer wieder die Fehler und Uebeltaten der Vergangenheit auf, um die noch groesseren Fehler und das noch groessere und wirkliche Uebel der Gegenwart unerkannt und unkritisiert zu begehen.

Sie reden viel von der Liebe und der Naechstenliebe und verbieten den Hass, um problemlos luegen und betruegen zu koennen und um ungehindert alles und jeden, der ihnen nicht dienstbar zu Willen ist, verachten, hassen und unterjochen zu koennen.

Sie sind das gottlose teuflisch satanische, das ehrlose unwuerdige auf ewig von Gott verfluchte und verdammte Ekelpack.

Sie sind weniger wert als der Hundekot auf der Strasse.

Der Heilige Geist Gottes bezeichnet sie als die ganz grosse Scheisse und den ganz grossen Dreck, ehrloses Ekelpack.

Logos Theon.

183.*1. ADVZ.*2008.-.21.06.

*

Die derzeitigen Friedensbemuehungen des Staates Israel sind lediglich kriegsstrategische Schachzuege, um sich fuer einen Krieg mit dem Iran den Ruecken frei zu halten. Und derart sind auch alle derzeitigen westlichen Friedensbemuehungen.

Der satanische Westblock will sich fuer einen Grosskrieg den Ruecken und die Flanken frei halten, Palaestina, Syrien und alles um den Staat Israel herum mit Friedens- und Waffenstillstands- und Nicht-Angriffsvertraegen absichern, um dann problemlos – wen immer man will – kriegerisch anzugreifen.

Und darum darf man mit dem Staat Israel auf gar keinen Fall Friednesvertraege und Nicht-Angriffspakte schliessen und ebensowenig mit Deutschland und den europaeischen Staaten; denn diese verpflichten andere zum Frieden, nur um problemlos selber den Krieg fuehren zu koennen. Und darum heisst es von nun an, sich in bestaendiger Kriegs- und Verteidigungsbereitschaft zu halten.!

*

Hoert, hoert die deutsche Luegenparole.:

“NIE WIEDER KRIEG.!”

“NIE WIEDER KRIEG VON DEUTSCHEM BODEN.!”

Sie haben nur gelogen und betrogen.

*

Der Frieden wird nun gesamtheitlich von der Erde genommen. Und das heisst, dass die gesamte Erde und Welt wieder in das reale Tierreich, die Verdammnis zurueckkehrt.

Der Heilige Logos hat gesprochen.

!!!. - OHNE WAHRHEIT IST UND BLEIBT MAN DER EWIGE VERLIERER - .!!!

!!!. - WER DIE WAHRHEIT HASST, DER IST VERFLUCHT - .!!!

© Gustav Staedtler./. 2008./.veritasindeum

!!!.---- VERITAS --- VENI --- VIDI --- VICI -----.!!!

*

mehr zum Thema.:

Atombombe oder Psychobombe.

http://wahrheitweltweit.blogg.de/eintrag.php?id=23

und

Die 3. Weltkriegsmission

http://wahrheitweltweit.wordpress.com/2008/01/23/die-3-weltkriegsmission/

und

HEIDEN, JUDEN, CHRISTEN, LUEGNER UND BETRUGSFASCHISTEN.

http://antichristentum.wordpress.com/2008/06/24/heiden-juden-christen-luegner-und-betrugsfaschisten/

*

Zur weiteren Verbreitung freigegeben.

*

»»»»»»»»»»»»»»»»»:«««««««««««««««««

Deutschland ist alles in allem vom Allerheiligsten Allmaechtigen Gott verflucht.

Deutschland das Land der Materialisten und Wohlstandsfaschisten.

»»»»»»»»»»»»»»»»»:«««««««««««««««««

Interview mit Professor Udo Steinbach

14.06.08

Ein Interview mit dem Universitätsdozenten, Professor Udo Steinbach, über die jüngsten Drohungen Israels gegen Iran.

http://german.irib.ir/index.php?option=com_content&task=view&id=11130&Itemid=35

The terms of use are hilarious - the bottom line seems to be that Google ends up owning excusively the entire aggregate work, but it is your fault if anything goes wrong.

Things like this make me tend to collaborate with OpenStreetMap rather than Google, even though it's aerial imagery has lesser detail resolution (for Indonesia, at least).

Sure, I believe that it's better for us all if Google has willingness to help openstreetmap and doesn't claim exclusive ownership to the maps.

The terms of use are hilarious - the bottom line seems to be that Google ends up owning excusively the entire aggregate work, but it is your fault if anything goes wrong.

[Terjemahan]Term of use-nya lumayan lucu. Intinya seluruh peta yang dihasilkan jadi hak milik eksklusif Google. Tapi kalo petanya salah, itu salahmu [/Terjemahan]

Hal macam ini yang mbuat aku cenderung berkontribusi ke OpenStreetMap biarpun resolusi foto udaranya yang disediakan oleh Yahoo masih kalah dengan Google.

Memang, idealnya Google mau mbantu OpenStreetMap dan bersedia menyajikan peta itu sebagai public domain.

Key elements in decision making

Decision making is the process that leads to a choice between a set of alternatives. Geographical decision-making means analysing and interpreting geographical information that is related to the alternatives in question. Decision making is often used in land suitability analysis, or site selection, as well as location allocation modelling.

Uncertainty

All decision making has a degree of uncertainty, ranging from a predictable (deterministic) situation to an uncertain situation (Malczewski, 1999). The latter one can be subdivided into stochastic decisions (which can be modelled by probability theory and statistics) and fuzzy decisions (which can modelled by fuzzy set theory and others). Consequently, particularly in uncertain situations, decision making involves the risk of making a "wrong" decision, because the information acquired is insufficient or the approach used is inappropriate. When uncertainty is part of the process, this uncertainty may in some cases be quantified and as such add another decision criteria to the evaluation process.

Objectives versus criteria

Most geographical decisions can de described in one of the following 3 categories (Fisher, 1999):

Single objective - Single criteria
Single objective - Multi criteria
Multi objective - Multi criteria

An objective is the ultimate goal that is to be achieved, i.e. finding the 5000 ha of land best suited for residential development. A criterion is a concrete descriptive variable from which the objective is inferred from, i.e. soil type, slope, proximity to roads, or development cost. In dealing with multiple criteria, the first obstacle in decision making is finding comprehensive criteria. The second obstacle is determining which criteria are more important than others and how to weight them.

Constraints versus factors

A constraint is a criterion that is absolute in its inclusion or exclusion of possible outcomes. This might be a boundary for a development area, slopes that are too steep or other attributes. A factor is a criterion that influences the suitability of the decision, according to its value. Different factors might be assigned with different weighting to illustrate their individual importance.

The decision making process

Decision making is a sequential process (based on Malczewski, 1999):

1. Defining the decision problem (objective)
2. Determining the set of evaluation criteria to be used
3. Weighting the criteria Generating alternatives
4. Applying decision rules
5. Recommending the best solution to the problem

Any decision-making process begins with the definition of the problem or the objective to be reached. Once the decision problem is defined, what follows is setting up a set of criteria that reflect all concerns of the problem and finding measures as to which the degree is achieved.

The purpose of weights is to express the importance or preference of each criterion relative to other criteria. Alternatives are often determined by constraints, which limit the decision space of feasible alternatives. Decision rules integrate criteria, weights and preferences to generate an overall assessment of the alternatives. Recommendations are based on a ranking of the alternatives, with reference to possible uncertainties or sensitivities. Sensitivities are changes in the input of the analysis that bias the outcome.

Weighting

Multiple criteria typically have varying importance. To illustrate this, each criterion can be assigned a specific weight that reflects it importance relative to other criteria under consideration. The weight value is not only dependent the importance of any criterion, it is also dependent on the possible range of the criterion values. A criterion with variability will contribute more to the outcome of the alternative and should consequently be regarded as more important than criteria with no or little changes in their range.

Weights are usually normalised to sum up to 1, so that in a set of weights (w1, w2, ., wn) =1.

There are several methods for deriving weights, among them (Malczewski, 1999):

• Ranking
• Rating
• Pairwise Comparison
• Trade-off

The simplest way is straight ranking (in order of preference: 1=most important, 2=second most important, etc.). Then the ranking is converted into numerical weights on a scale from 0 to 1, so that they sum up to 1.

IDRISI features a weight routine to calculate weights, based on the pairwise comparison method, developed by Saaty (1980). A matrix is constructed, where each criterion is compared with the other criteria, relative to its importance, on a scale from 1 to 9. Then, a weight estimate is calculated and used to derive a consistency ratio (CR) of the pairwise comparisons, If CR > 0.10, then some pairwise values need to be reconsidered and the process is repeated till the desired value of CR < 0.10 is reached.

Multi-criteria / single objective decision making

The most used analytical facilities in a GIS is overlaying several thematic layers to find areas that are common to given criteria, or areas that exclude each other. In finding the solution to achieve one objective this is often done by sieve mapping, sifting through the layers to find location that encompass all desired criteria.

Boolean constraints

The easiest way to do sieve mapping to use Boolean logic to find combinations of layers that are defined by using logical operators: AND for intersection, OR for union, and NOT for exclusion of areas (Jones, 1997). In this approach, the criterion is either true or false. Areas are designated by a simple binary number, 1, including, or 0, excluding them from being suitable for consideration (Eastman, 1999).

Boolean operators

Each criteria is of equal value

Within 500m from Shepshed

Within 450m from roads

Slope between 0 and 2.5%

Land grade III

Suitable land, min 2.5 ha

Fuzzy variables

This approach attempts to turn the artificially crisp and clear-cut criteria of the simple Boolean approach into real-life continuous criteria that express a degree of suitability. Criteria can be modelled as a continuous variable ranging from most suitable (value 1) to least suitable (value 0).

Fuzzy approach

Bright areas have highest suitability

100m < Shepshed <1000m

Between 50m and
600m to roads

Slope between 1 and 5%

Land grade III and grade IV

Varying suitability,
min 2.5 ha

Comparison of different approaches

Boolean constraints

The Boolean constrains leave no room for prioritisation, all suitable areas are of equal value, regardless of their position in reference to their factors.

Minimal membership

In the minimal fuzzy membership the minimum suitability value from each factor at that location is chosen from as the "worst case" suitability. This results in larger areas, with highly suitable areas, with area that are more suitable shown in a brighter colour.

Probabilistic intersection

The probabilistic fuzzy intersection has less suitable areas than the minimal fuzzy operation. This is due to the fact that this effectively is a multiplication. Multiplying suitability factors of 0.9 and 0.9 at one location yields an overall suitability of 0.81, whereas the fuzzy approach results in 0.9. Thus, it can be argued that the probabilistic operation is counterproductive when using fuzzy variables (Fisher, 1994). When using suitability values larger than 1 this does of course not occur.

Weighted Overlay

The weighted overlay produces many more areas. This shows all possible solutions, regardless whether all factors apply or not, as long as at least one factor is valid for that area. This is so, because even if one factor is null, the other factors still sum up to a value. This also shows areas that are outside of the initial constraints.

Multi-criteria / Multi-objective decision making

The multi-objective decision making process is similar to single objective. One added aspect is that objectives can be complementing, but more often they are conflicting, and a compromising solution must be sought.

Simple Additive Weighting

Simple Additive Weighting (SAW) or Weighted Linear Combination (WLC) is the most often used technique in multi-criteria decision making. Criteria here may include weighted factors and constraints.

Calculating the product of weight and factor multiplied with all constraints at any location, and then summing up all products yields a total overall score. The score for each alternative A is:

A = SUM (wi * xi) or A = SUM (wi * xi) * SUM (cj)
if a constraint is part of the decision

xi = criterion score of factor i,

wi = weight of factor i,

cj = criterion score of constraint j

When applying SAW on fuzzy variables the formula can be applied directly, because the values all are in range from 0 to 1and directly comparable. In other cases the values for each criterion range must be standardised on the same scale. This is achieved by stretching the values so that they meet the same scale. If the continuous scale ranges from 0 to 255, a location with value of 123 in proximity to road and a location with a value of 123 in slope gradient means that the two locations initially, before SAW is applied, are equally suitable with respect to their criteria. This is why factor maps need to be stretched before they can be used in the MCE module.

WLC, single objective:
Best 1000 ha for industrial development

Standardised factors (constraints not shown):

Proximity to roads

Proximity to town

Slopes

Distance from park

Suitability maps:

SAW, result:
Area suited for industry

Ranking yields:
Best 1000 ha for industry

The above shows an ordinary weighted linear combination for a single objective. The image series below demonstrates that when multiple objectives must be met, the suitability for each objective can be used as factor for the multi-objective task.

WLC, Complementary objectives:

Best 4000 ha for both industrial and residential development

Standardised factors:
(constraints not shown)

Suitability for industrial development, standardised from suitability for industry above

Suitability for residential development, standardised from suitability for residential development, derived in similar manner as industrial suitability above.

Suitability maps:

SAW: Suitability for urban and residential development

Best 4000 ha for urbanisation

When objectives are conflicting, the higher priority is solved first, and then used as a mask for the lower priority in its calculation. Thus, there will be no overlapping, but zoning of allocations.

Conflicting objectives
Protecting 6000 ha of agriculture from urbanization,

Urbanisation has priority over agriculture

Standardised factors
(constraints not shown):

Proximity to roads

Proximity to town

Slopes

Distance from park

Rainfall

Suitability maps:

Suitable for urbanisation, derived from above

Suitable for agriculture

Suitable for agriculture, with the best 4000 ha for urbanisation masked out

Best 6000 ha for agriculture, with priority to 4000 ha for urbanisation

Final allocation of land for urbanisation and agriculture

Multi Objective Land Allocation

Multi Objective Land Allocation (MOLA) provides a procedure for solving multi-objective land allocation problems for cases with conflicting objectives. It determines a compromise solution that attempts to maximize the suitability of lands for each objective with respect to their assigned weights. In practical terms, after each objective has been given its suitability, MOLA will allocate areas from each objective according to their ranking and weighting, using an iterative process.

Making a pairwise comparison, MOLA resolves any conflict by allocating each cell to the objective to which its total weighted suitability is nearest. As a result, any particular objective will lose some conflict cases because it will need to accept cells that are of lower rank for itself, but of higher rank for any other objective (Jones, 1997).

MOLA, Conflicting objectives:
Protecting 6000 ha of agricultural land
while leaving 1500 ha for industrial development

Step 1
Standardised factors:

Proximity to water

Proximity to power

Proximity to roads

Proximity to market

Slope

Step 2
Suitability for each objective:

Agriculture

Carpet industry

Best 6000 ha for agriculture

Best 1500 ha for carpet industry

Conflict area

Step 3
MOLA

Compromise solution

It can be noted that industry is located particularly close to where roads and rivers coincide. This is consistent with the fact that proximity to water and power respectively has the highest weighting for agricultural development and industrial location respectively, since power lines were assumed to be along major roads.

Discussion

In the Boolean Intersection all criteria are assumed to be constraints. Suitability in one constraint will not compensate for non-suitability in any other constraint. This procedure also seems to carry the lowest possible uncertainty since only areas considered suitable in all criteria are entered into the result. However, this method requires crisp entities as criteria, a requirement that may be hard to meet. The advantage of the Boolean Intersection is that is straightforward and easy to apply. A disadvantage is that it might exclude or include areas that are not truly represented. Boolean Intersection is best applied either as a crude estimation or when all factors are of equal weight and when it can be assumed that the factors are of equal importance in any of the area they cover.

Weighted Linear Combination allows each factor to display its potential because of the factor weights. Factor weights are very important in WLC because they determine how individual factors will aggregate. Thus, deciding on the correct weighting becomes essential. The advantage of this method is that all factors contribute to the solution based on their importance. The aggregation of individual weights is prone to be very subjective, even when pairwise comparison is used for ensuring consistent weights.

Multi Objective Land Allocation blends priorities, whereas WLC favors one over the other, creating zones that do not overlap. MOLA is therefore preferable for solving conflicts that arise when multiple objectives whish to acquire the same area and where an incorrect decision might be highly damaging.

Conclusion

A GIS has the capacity to integrate information form a variety of sources into a spatial context and is well suited to support decision making procedures. GIS can act as a tool in helping the decision-makers evaluate alternatives, visualise choices and explore certain alternatives.
In the end, it is the decision maker, who determines the criteria, the factors, the constraints, the individual weighting and the decision rules.

There is also a multitude of other approaches to decision making not mentioned in this essay. Consequently, there is great room for bias in the decision making process.

References

Eastman, J.R. (1999) Multi-criteria evaluation and GIS

Longley, Goodchild, Maguire and Rhind (Eds.) Geographical Information Systems: Principles, Techniques, Applications and Management, 2nd Edition, Vol. 1, pp 493 -502

Fisher, P.F. (1994) Probable and fuzzy models of the viewshed operation. Innovations in GIS 1, pp. 161-175

Fisher, P.F. (1999) GY 705 Geographical Decision Making, University of Leicester, Lecture notes and practical tutorials

Jones, C. (1997) Geographical Information Systems and Computer Cartography, pp. 215 - 230

Malczewski, J. (1999) GIS and Multicriteria Decision Analysis