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Error using EnableEditorTracking

Error using EnableEditorTracking


Using code from http://resources.arcgis.com/en/help/main/10.1/index.html#//00170000016p000000 trying to enable editor tracking. It works fine if there is no feature dataset in the GDB, but the moment I add a feature dataset in the GDB I get the following error:

[u'QGC_LINK', u'UDM'] Test Enabling editor tracking on QGC_LINK Line 60 Failed to execute. Parameters are not valid. ERROR 000110: QGC_LINK does not exist Failed to execute (EnableEditorTracking).

It seems that it can't find the file path.

The file structure is as follows:

Test.gdb Test.gdb/UDM Test.gdb/QGC_Link Test.gdb/FD/UDM_1

Code as follows:

import arcpy, os # Set the workspace workspace = arcpy.GetParameterAsText(0) # Set the workspace environment arcpy.env.workspace = "M:/GIS/Test.gdb/" # Get all the stand alone tables and feature classes dataList = arcpy.ListTables() + arcpy.ListFeatureClasses() print dataList # For feature datasets get all of the featureclasses # from the list and add them to the master list for dataset in arcpy.ListDatasets("", "Feature"): arcpy.env.workspace = os.path.join(workspace,dataset) dataList += arcpy.ListFeatureClasses() print dataset # Execute enable editor tracking for dataset in dataList: print 'Enabling tracking on ' + dataset arcpy.EnableEditorTracking_management(dataset, "Created", "CreatedDate", "Modified", "ModifiedDate", "ADD_FIELDS", "UTC") print 'Enabling complete'

The problem with the code is that you are setting the arcpy.env.workspace to be your geodatabase path + feature dataset name before iterating the feature dataset (good), but this workspace is used then when trying to enable editor tracking on an object not within the feature dataset (not good). Of course the object cannot be found, because arcpy is trying to find a feature class in geodatabase without looking into the feature dataset.

What you have to do is either enable tracking on datasets within feature dataset individually or create a list with the full path to the datasets and then use the elements of this list as "dataset" in the Enable Editor Tracking function. In the code sample below, I iterate over the datasets within the feature datasets first.

import arcpy, os # Set the workspace workspace = r"C:ArcGISDefault.gdb" # Set the workspace environment arcpy.env.workspace = r"C:ArcGISDefault.gdb" # Get all the stand alone tables and feature classes dataList = arcpy.ListTables() + arcpy.ListFeatureClasses() print dataList # For feature datasets get all of the featureclasses for dataset in arcpy.ListDatasets("", "Feature"): arcpy.env.workspace = os.path.join(workspace,dataset) FDdataList = arcpy.ListFeatureClasses() print FDdataList for dataset in FDdataList: print '**Enabling tracking on ' + dataset arcpy.EnableEditorTracking_management(dataset, "Created", "CreatedDate", "Modified", "ModifiedDate", "ADD_FIELDS", "UTC") print '**Enabling complete' # Execute enable editor tracking on non-FD objects arcpy.env.workspace = r"C:ArcGISDefault.gdb" #have to set back to the geodatabase! for dataset in dataList: print '**Enabling tracking on ' + dataset arcpy.EnableEditorTracking_management(dataset, "Created", "CreatedDate", "Modified", "ModifiedDate", "ADD_FIELDS", "UTC") print '**Enabling complete'

Abstract

This study examines the impact of radio-frequency identification (RFID) technology on the inventory control practices of a small-to-medium retailer using a proof of concept (PoC) approach. The exploratory study was conducted using a single case study of a hardware retailer stocking 5000 product lines provided by 110 active suppliers. To analyze the present mode of operation, procedural documents, semi-structured interviews and a participant observation was conducted. The basis for the proof of concept was a future mode of operation using a quasi-experimental design. Results indicate that in a small-to-medium retail environment, RFID technology could act as a loss prevention mechanism, an enabler for locating misplaced stock, and make a significant contribution to the overall improvement of the delivery process.


Black Friday: Edmonton's emergency ops have come a long way

In emergency situation like the Edmonton tornado, there are two main sites where the action is taking place — the disaster site itself and the hub where the relief is co-ordinated.

In 1987, when Black Friday descended upon the city, information was fleeting for those in the various emergency operations centres.

For Mike Cook, information came in the form of first-hand experience as he entered the Nault lumber yard.

"I was actually sent to a report of power lines down on 34 Street and about 62 Avenue. When I rolled in there, not only were the power lines down but all the buildings. Basically it flattened a whole industrial area," said Cook, who was a constable with the Edmonton Police Service at the time.

Cook now part of the emergency operations centre in Edmonton's downtown core and said a lot has changed in the past 25 years.

"What was lacking back then and what we've accomplished now is we have a co-ordination of what's happening and we have the support of those people in the field. So when they need something we're scrambling in here and finding it for them," Cook explained.

The work of the first responders is just as important as always but today those police, fire and EMS personnel have a central manager sending them resources as they are needed.

"At the beginning of an emergency, as it's coming in, we'll get a notification and start making an assessment on the seriousness of the emergency," he said from the monitor-filled room that houses emergency managers when an incident occurs.

"Then, depending on the magnitude of the incident, this room will start to move into activation level which will bring in senior officials from all the city departments."

An incident is seen to first by the municipality. Once their resources run out, the province is asked for help and, in extreme events, the federal government assists as well.

Emergency centre

Having one emergency centre helps make that possible.

"It's to make sure that all of the departments are talking to each other. It's much easier if they're face to face and when they're in this environment they have access to all our computer networks and communications and we can get a consistent message out to the public," Cook said.

Since Black Friday, new advancements including Doppler radar weather tracking, geographic information systems and the widespread use of the Internet have all become commonplace. Added to that, the emergency public warning system was developed following Black Friday and is now being used by locales across Canada.

Cook said the Edmonton operations centre uses social media to get a better grasp on a situation from people at ground level.

"We monitor it 24-7, particularly during an event," he said, adding when wind brought down a stage at Big Valley Jamboree, killing a spectator, they were using Facebook and Twitter to get a feel for the situation from afar.


Introduction

Online social networks (OSNs) have become extremely popular. According to Nielsen Online’s research [7], social media have pulled ahead of email as the most popular online activity. More than two-thirds of the global online population visit and participate in social networks and blogs. In fact, social networking and blogging account for nearly 10% of all time spent on the Internet. These statistics suggest that OSNs have become a fundamental part of the global online experience.

Through OSNs, users connect with each other, share and find content, and disseminate information. Numerous sites provide social links, for example, networks of professionals and contacts (e.g., LinkedIn, Facebook, MySpace) and networks for sharing content (e.g., Flickr, YouTube).

Understanding how users behave when they connect to these sites is important for a number of reasons. First, studies of user behaviors allow the performance of existing systems to be evaluated and lead to better site design [54], [12] and advertisement placement policies [35]. Second, accurate models of user behavior in OSNs are crucial in social studies as well as in viral marketing. For instance, viral marketers might want to exploit models of user interaction to spread their content or promotions quickly and widely [42], [34], [35]. Third, understanding how the workload of social networks is re-shaping the Internet traffic is valuable in designing the next-generation Internet infrastructure and content distribution systems [41], [33].

Despite the potential benefits, little is known about social network workloads. A few recent studies examined the patterns using data that can be gathered from OSN sites, for instance, writing messages to other users [54], [19], [52], [28]. As a result, these studies reconstruct user actions from “visible” artifacts like messages and comments. While these studies yield insights into social network workload, they do not provide a global picture of the range and frequency of activities that users conduct when they connect to these sites.

A complementary approach to study OSN workloads is to use traces such as clickstream data that capture all activities of users [18]. Since clickstream data include not only visible interactions, but also “silent” user actions like browsing a profile page or viewing a photo, they can provide a more accurate and comprehensive view of the OSN workload.

In this paper we present an in-depth analysis of OSN workloads based on a clickstream dataset collected from a social network aggregator. Social network aggregators are one-stop shopping sites for OSNs and provide users with a common interface for accessing multiple social networks. Because social network aggregators are an excellent measurement point for studying workloads across various OSNs, we collaborated with a popular social network aggregator in Brazil for this study. We obtained a clickstream dataset, which described session-level summaries of over 4 million HTTP requests during a 12-day period in 2009. The dataset included activity data for a total of 37,024 users who accessed various OSNs through the social network aggregator.

Using the clickstream data, we conducted three types of analyses. First, we characterized traffic and session patterns of OSN workloads (Section 4). We examined how frequently people connect to OSN sites and for how long. Based on the data, we provide best fit models of session inter-arrival times and session length distributions. Second, we developed a new analysis strategy, which we call the clickstream model, to characterize user activity in OSNs (Section 5). The clickstream model captures dominant user activities and the transition rates between activities. We profiled user activities for four OSN services: Orkut, MySpace, Hi5, and LinkedIn. Third, to gain insight into how users interact within a given social network, we additionally collected the Orkut website and analyzed user activity along the social graph (Section 6). Our analysis reveals how often users visit other people’s online profiles, photos, and videos. We also show that, in terms of physical distance, users usually interact mainly with local friends.

This paper provides many interesting findings: (1)

Session duration, inter-request time, and inter-session time are heavy-tailed, indicating large variations in the OSN usage among users. We provide best-fit distributions for these measures in order to provide models able to reproduce activity in Orkut sessions.

Using clickstream data, we present the frequency, sequence, and duration of user activities in Orkut. We find that browsing, which cannot be inferred from publicly available data, is the most dominant behavior (92%). We also noted that users tend to repeat activities and perform a small subset of related activities per session.

When we consider silent interactions like browsing friends’ pages, the number of friends a user interacts with increases by an order magnitude, compared to only considering visible interactions.

Analysis of user interaction along the social graph shows that Orkut users not only interact with 1-hop friends, but also have significant exposure to friends that are 2 or more hops away (22%).

The analysis of user interaction along the physical distance suggests that users mostly interact with users located within a close geographical distance. This means that while content in OSNs is created across geographically diverse regions, it is consumed locally.

In summary, our study provides an in-depth look into the usage of OSN services from the viewpoint of a social network aggregator. The clickstream data analyzed in the paper provides an accurate view of how users behave when they connect to OSN sites. Furthermore, our data analysis suggests several interesting insights into how users interact with friends in Orkut. We believe that our findings have implications for efficient system and interface design as well as for advertisement placement in OSNs.


Block Group Demographic Data Analytics

.. use tools described here to access block group data from ACS 2016 (or ACS2017 in December 2018) using a no cost, menu driven tool accessing the data via API. Select from any of the summary statistic data. Save results as an Excel file or shapefile. Add the shapefile to a GIS project and create unlimited thematic pattern views. Add your own data. Join us in a Data Analytics Web session where use of the tool with the ACS 2017 data is reviewed.

See related Web section for more details.
– examine neighborhoods, market areas and sales territories.
– assess demographics of health service areas.
– create maps for visual/geospatial analysis of locations & demographics.

Illustration of Block Group Thematic Pattern Map – make for any area

– click graphic to view larger view
– pointer (top right) shows location of Amazon HQ2

Topics in this how-to guide (links open new sections/pages)
• 01 Objective Thematic Pattern Map View
• 02 Install the CV XE GIS software
• 03 Access/Download the Block Group Demographic-Economic Data
• 04 Download the State by Block Group Shapefile
• 05 Merge Extracted Data (from 03) into Shapefile (04)
• 06 Add Shapefile to the GIS Project Set Intervals
• 07 Viewing Profile for Selected Block Group
• 08 BG Demographics Spreadsheet
• 09 Block Group Demographics GIS Project
• 10 Why Block Group Demographics are Important

Data Analytics Web Sessions
Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.


Important Upcoming Data Releases: September 2017

.. monthly updates on recent & upcoming data analytics tools & resources .. this section provides a monthly update on important new data developments and applications/developments to further their use in data analytics. A focus of this section is on new or revised geographic, demographic and economic data. Most of these data are used to develop and update ProximityOne census tract-level up demographic-economic projections to 2022 and county-level up population by single year of age projections to 2060. See about September projection updates below on this page. This section is organized into recent past data updates and upcoming (month ahead) data releases and may be updated to reflect new or extended details. See related news and updates:
• What’s New daily updates
• Situation & Outlook Calendar

Recent Past Data Releases/Access

U.S. by Census Tract 2017 HMDA Low & Moderate Income (FFIEC)
• Release date — 8/17 next update — mid 2018
• 2017 annual HMDA data — covers all income levels not only LMI
• New 2017 HMDA data
• See more information – access data.

U.S. by County Population by Single Year of Age (NCHS)
• Release date — 8/22/17 next update — mid 2018
• 2010 through 2016 annual population by single year of age
• New 2016 data extending annual series 2010 forward
• See more information – access updates.

Housing Price Index (FHFA)
• Release date — 8/22/17 next update — 11/28/17
• Quarterly HPI
• New 2017Q2 data extending quarterly time series.
• See more information – access updates.

Quarterly Gross Domestic Product by State (BEA)
• Release date — 9/20/17 next update — 11/21/17
• Quarterly GDP by Industry
• New 2017Q1 data extending quarterly time series.
• See more information – access data.

2017 TIGER Digital Map Database (Census)
• Expected

9/7/17
• Topologically Integrated Geographic Encoding & Referencing (TIGER) data.
• Geographic data predominately shapefiles.
.. intersection to intersection road segment geography and attributes.
• New 2017 GIS/mapping shapefiles for use with wide-ranging data
.. including with Census 2010, ACS 2016 & other subject matter.
• See more information – updates to access summarized in that section.

Census of Employment and Wages (BLS/CEW)
• Release date — 9/6/17 next update — 12/5/17
• AKA ES-202 data — establishments, employment & wages by NAICS code/type of business
• U.S. by county.
• New 2017Q1 data extending quarterly time series.
• See more information.

2016 American Community Survey 1-year estimates (Census/ACS)
• Release date — 9/14/17
• Wide-ranging demographic-economic data for areas having population 65,000+
.. all states, CDs, PUMAs, MSAs and larger cities/CBSAs/school districts/counties (817 of 3142)
• New 2016 estimates.
• See more information – updates to access summarized in that section.

SY 2015-16 Annual School & School District Characteristics (NCES)
• Expected

9/14/17
• National school school & school district characteristics.
• New 2015-16 school year administratively reported data.
• Schools … see more information – access updates.
• School District … see more information – access updates.

2016 Annual Gross Domestic Product by Metro (BEA)
• Release date — 9/20/17
• GDP by Industry by Metro
• New 2016 data extending time series
• See more information – access updates.

Census Tract Estimates and Projections to 2022 — ProximityOne
• Release data

9/27/17
• National census tract and higher level geography demographic-economic updates
• Annual estimates & projections 2010 through 2022
• Updated to reflect/integrate data released through 9/2017 as summarized above • See more information.

County Population by Single Year of Age Projections to 2060 — ProximityOne
• Release data

9/27/17
• National county and higher level geography demographic updates
• Annual estimates & projections 2010 through 2060
• Updated to reflect/integrate data released through 9/2017 as summarized above. • See more information.

Notes [goto top]
– BEA – Bureau of Economic Analysis
– BLS – Bureau of Labor Statistics
– Census – Census Bureau
– FFIEC – Federal Financial Institutions Examination Council
– FHFA – Federal Housing Finance Agency
– NCES – National Center for Education Statistics
– NCHS – National Center for Health Statistics

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.


Error using EnableEditorTracking - Geographic Information Systems

Authors: Mehmet Savsar Aaya Aboelfotoh Dalal Embaireeg

Addresses: College of Engineering and Petroleum, Industrial and Management Systems Engineering, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait ' College of Engineering and Petroleum, Industrial and Management Systems Engineering, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait ' College of Engineering and Petroleum, Industrial and Management Systems Engineering, Kuwait University, P.O. Box 5969, Safat 13060, Kuwait

Abstract: Most companies, which need to distribute their production daily, solely rely on human judgment in scheduling customer orders by assigning a delivery vehicle and selecting the routes for those vehicles. With increasing demand, this approach quickly becomes error prone. In this study, we present analysis of a distribution system and propose a systematic approach to improve distribution of tasks using geographic information system (GIS). Specifically, ArcMap's network analyst tool is used in order to minimise total transportation costs and ensure workload balance. We incorporate dynamic traffic conditions, time windows, vehicle capacity and driver working hours into our model to present more realistic results. We compare the total transportation costs due to manual assignments with the costs obtained using our approach, in addition to proving the tool's validity for problems of a larger scale. Analysis is applied to a specific food catering company in order to illustrate the procedure in detail.

Keywords: distribution vehicle routing time windows transportation geographic information system GIS network analyst traffic capacitated food industry delivery management.

International Journal of Applied Management Science, 2019 Vol.11 No.2, pp.124 - 152

Accepted: 05 Jun 2018
Published online: 06 Feb 2019 *


Error using EnableEditorTracking - Geographic Information Systems

Comparing a single-stage geocoding method to a multi-stage geocoding method: how much and where do they disagree?

6 1 12 http://www.ij-healthgeographics.com/content/6/1/12

2007 Lovasi et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Geocoding methods vary among spatial epidemiology studies. Errors in the geocoding process and differential match rates may reduce study validity. We compared two geocoding methods using 8,157 Washington State addresses. The multi-stage geocoding method implemented by the state health department used a sequence of local and national reference files. The single-stage method used a single national reference file. For each address geocoded by both methods, we measured the distance between the locations assigned by each method. Area-level characteristics were collected from census data, and modeled as predictors of the discordance between geocoded address coordinates.

The multi-stage method had a higher match rate than the single-stage method: 99% versus 95%. Of 7,686 addresses were geocoded by both methods, 96% were geocoded to the same census tract by both methods and 98% were geocoded to locations within 1 km of each other by the two methods. The distance between geocoded coordinates for the same address was higher in sparsely populated and low poverty areas, and counties with local reference files.

The multi-stage geocoding method had a higher match rate than the single-stage method. An examination of differences in the location assigned to the same address suggested that study results may be most sensitive to the choice of geocoding method in sparsely populated or low-poverty areas.

Using a sample of 8,157 Washington State addresses, we compared the WA DOH multi-stage geocoding method to a single-stage geocoding method based on a single street reference file. We expected to find a higher geocoding match rate with the WA DOH multi-stage process. For addresses geocoded by both methods, we measured the distance between multi-stage and single-stage geocoded coordinates for the same address we use this "discrepancy-distance" to quantify disagreement between the two methods. We expected that the multi-stage and single-stage geocoded address coordinates would be more similar, and discrepancy-distances smaller, in more densely populated areas and areas where national street files were used as a reference for both geocoding methods. Further, we hypothesized that the two geocoding methods would disagree less, as indicated by smaller discrepancy-distances, in low poverty areas.

Of the 8,157 Washington State addresses, we were able to geocode 8,098 (99%) by at least one method and 7,686 (95%) addresses by both methods. The multi-stage geocoding process matched 8,058 (99%) of the addresses, and the single-stage geocoding method matched 7,726 (95%).

While we included addresses in each of the 39 counties in Washington State, more of the geocoded addresses were in the densely populated counties. According to Census data from the year 2000, Washington State had an overall density of 34 residents per square kilometer 68% of our geocoded addresses were in the 8 counties with densities higher than 34 residents per square kilometer. In the state, 10% of the population was below the federal poverty line 51% of our geocoded addresses were in counties with less than 10% poverty.

Compared to addresses geocoded by both methods, those geocoded by only the multi-stage method were less likely to be in counties with parcel data (Table 1 ) addresses geocoded only by the single-stage method were less likely to be in counties with local street data and tended to be in sparsely populated areas. Addresses geocoded by only one method, rather than both methods, also tended to be in areas with higher poverty rates.

Area characteristics for geocoded addresses

Multi-stage method only

Single-stage method only

Local street data available, %

Density, median, population/km 2

Area characteristics were based on the multi-stage geocoded address coordinates when available (N = 8,058) and the single-stage geocoded address coordinates otherwise (N = 40).

For those addresses matched by both methods, 96% (7,374) were geocoded to the same census tract by each method of addresses in the same census tract, 93% (6,859) were geocoded to the same census block group by both methods. The density and percent poverty based on the two geocoding results generally agreed as well: for density, intraclass correlation coefficients were 0.97, and 0.93 at the census tract and census block group levels, respectively for percent poverty, intraclass correlation coefficients were 0.97 and 0.89 at the census tract, and census block group levels.

Although the locations assigned by the two geocoding methods differed, these differences did not take the form of a systematic shift in one direction (Figure 1 ). A cloud of dots shifted off-center relative to the reference circles would have suggested systematic bias. While there was no single direction bias, however, we did observe clustering along the north-south and east-west axes (Figure 2 ). A post-hoc examination of subgroups suggested that the clustering along axes was most pronounced for counties where the multi-stage method used local roads for geocoding. We confirmed this "plus" pattern in a repeat analysis using identical offsets for both geocoding methods.

Distance and directional bias between geocoded address coordinates for multi-stage and single-stage geocoding methods

Distance and directional bias between geocoded address coordinates for multi-stage and single-stage geocoding methods: This figure shows one dot for each address geocoded by both methods, with reference circles at 0.5 and 1.0 km. The multi-stage geocoded address coordinates was centered as a reference, and the dots used to show the relative position of the single-stage address coordinates for the same address. Dots close to the middle (0,0) represent small discrepancy-distances and high concordance between the two methods. Dots directly above the center had single-stage geocoded address coordinates further north than their multi-stage coordinates. Dots randomly scattered in all directions would indicate no directional bias, whereas an off-center cluster of dots would indicate systematic bias between the two methods. Addresses with discrepancy distances greater than 2 km were not included in this figure (N = 78).

Directional bias between geocoding methods

Directional bias between geocoding methods: This figure shows an angular histogram with radial lengths proportional to frequencies of shifts in each direction between the single-stage and multi-stage geocoded address coordinates. A light circle is drawn at the mean frequency for reference.

The median discrepancy-distance between locations assigned by the two methods was 54 meters, and the 90 th and 95 th percentiles were 180 and 296 meters (Table 2 ). The discrepancy-distance was less than 1 km for 98% of the addresses. Discrepancy-distances were largest in low-density census tracts (Figure 3a ). The associations of discrepancy-distance with census tract poverty and multi-stage data source were not consistent. Medians and 90 th percentiles suggested that discrepancy-distances were larger in areas with less poverty, but the means and proportion larger than 1 km suggested the largest discrepancy distances were for areas with between 10 and 20% poverty (Table 2 , Figure 3b ). Areas where the WA DOH multi-stage process used local parcel data had a higher median discrepancy-distance, but fewer discrepancy-distances were greater than 1 km in these counties.

Distance between locations for the same address assigned by two geocoding methods, by area characteristics

Discrepancy-distance (m)

Census Tract Density* (population/km 2 )

Most common reference for multi-stage method*

Discrepancy-distance indicates distance between multi-stage and single-stage geocoded address coordinates for the same address p50, p90, p95, and p99 indicate the 50 th , 90 th , 95 th , and 99 th percentiles TIGER indicates Topologically Integrated Geographic Encoding and Referencing system line files, this includes NAVTEQ and Dynamap reference files

* Indicates significance (p < 0.05) for a comparison of discrepancy-distances across subgroups using a linear regression model

Discrepancy-distance distributions by category of (a) density and (b) poverty

Discrepancy-distance distributions by category of (a) density and (b) poverty: This figure shows a smoothed kernel density (similar to a histogram) for discrepancy-distances by category. Large discrepancy-distances indicate disagreement between the single-stage and multi-stage geocoding methods. Density is categorized using the number of residents per square kilometer in the census tract. Poverty is categorized using the percent of residents below the poverty line in the census tract.

In multiple regression, accounting for poverty, density, and multi-stage reference file simultaneously, we found larger discrepancy-distances for (1) areas with lower density, (2) areas with a lower percent poverty, and (3) counties with local parcel data or local roads (Table 3 ). As density doubled, the median discrepancy-distance decreased by approximately 10%, indicating closer agreement in densely populated areas for the two geocoding methods. As poverty doubled, the median discrepancy-distance decreased by approximately 13%, indicating closer agreement between geocoding methods in high poverty areas. Where the multi-stage geocoding method used mainly TIGER-based street files, median discrepancy-distance was halved compared to counties where local parcel files were the most common reference. When corresponding models were run at the county and census block group levels, estimates for density and poverty remained significant and estimates were in the same direction.

Multi-variable regression model of discrepancy-distance

Ratio of discrepancy-distance medians* (95% CI)

Poverty in census tract

Density of census tract

Multi-stage reference file

Discrepancy-distance indicates distance between multi-stage and single-stage coordinates for the same address TIGER indicates Topologically Integrated Geographic Encoding and Referencing system line files, this includes NAVTEQ and Dynamap reference files

* Ratios lower than one indicate that a category or characteristic was associated with a smaller discrepancy-distance (closer agreement between the two geocoding methods)

In stratified analyses, the effects of poverty and density were in the same direction, but were most pronounced where both geocoding methods used TIGER-based street files: median ratio was 0.81 (95% CI: 0.75 – 0.86) for doubling poverty in this subgroup (likelihood ratio test p for interaction: < 0.001) for a doubling of density and the ratio was 0.86 (95% CI: 0.83 – 0.88).

We found that a multi-stage geocoding method implemented by the WA DOH achieved a match rate 4% higher than that achieved by a single-stage method. Most addresses were matched by both methods, but they were not geocoded to exactly the same coordinates by each method: 10% of addresses were assigned locations at least 180 meters apart by the multi-stage and single-stage methods, and 2% of addresses were assigned locations at least one kilometer apart. Locations assigned by the two methods were closer together in high density and high poverty areas, and in areas where reference data sources were most similar for the two methods. The results for area-level poverty, which were contrary to our hypothesis, were not explained by density or availability of local reference files. The associations of area-level poverty and density with discrepancy-distance were strongest where the two methods used similar reference files.

Our study investigated whether single-stage geocoded address coordinates were systematically shifted relative to the multi-stage address coordinates. We found that the single-stage coordinates were shifted north-south and east-west relative to the multi-stage coordinates more often than would be expected by chance alone. This may have been due to different assumptions about how addresses are spaced along a street 14 , since WA streets are more likely to be oriented in the cardinal directions than would be expected by chance. In Washington State, we estimated that 42 percent of street segments are within five degrees of being oriented directly north-south or east-west (only 11 percent expected by chance). This directional shift finding may be most relevant to areas where urban planners played an active role in establishing N-S and E-W roadway grids.

Another limitation was that there may have been unmeasured or residual confounding by address or area characteristics in this study, interfering with our ability to assess which characteristics predicted geocoding discrepancies. Finally, the geographic scope and distribution of our study addresses limits the generalizability of this Washington-based study 4 . These data were statewide and may be similar to other health department address data however, the geocoded addresses were all numbered street addresses with ZIP codes and did not include Post Office boxes.

The importance of the differences between any two methods depends on the context and purpose of geocoding. Both the level of analysis and hypothesized exposure effects will influence the cost of geocoding errors. The available data or confidentiality protections may constrain some researchers to work with data at the zip code or census tract level, or even "jittered" address locations with deliberately introduced error. Some researchers might find our 96% concordance at the census tract level encouraging. Single-stage geocoding using street addresses may be adequate for some research purposes. However, for a study of small-scale environmental exposures, such as radiation, the ability to detect or replicate an association may depend on the geocoding method selected, and even multi-stage geocoding may place addresses far from their actual locations. The importance of relative geocoding precision may also vary across areas. For example, the commonly observed pattern of decreased geocoding accuracy in sparsely populated areas may be of little concern if an exposure, such as air pollution, is less variable across small distances in a rural context. Geocoding match rates which vary among geocoding methods can also affect the power and external validity 6 of spatial epidemiology studies subjects with unmatched addresses may not be representative and are generally excluded from further analyses.

The multi-stage geocoding method examined in our study had the advantage of a higher match rate, but without a gold standard with which to gauge the accuracy of the two geocoding methods we could only guess the relative validity of the two methods. Our findings and those of previous studies suggest that the choice of geocoding method may be especially influential in areas with low density or low poverty.

A sample of addresses throughout Washington State was geocoded using the multi-stage WA DOH method and a single-stage method within a GIS software package. This convenience sample included 8,753 addresses of licensed daycare providers in Washington State, collected from 2003� by the Children's Administration of the Washington State Department of Social and Health Services.

All addresses had an accompanying ZIP code, but two were street intersections and 156 others had no street number. The addresses without street numbers were not geocoded by either method. There were also 475 Post Office boxes and two Mail Stop numbers. After intersections, addresses without a street number, Post Office boxes and Mail Stops were excluded, 8,157 addresses remained in our analyses.

To assess data accuracy/availability at the county level, we recorded the type of reference file (local parcel, local street, or TIGER-based street file) most commonly used for the multi-stage geocoding process for each county. We also categorized counties according to whether local parcel and street data, collected from 2000 to 2003, were available to WA DOH at the time of multi-stage geocoding.

We used the default geocoding setting to identify exact or approximate ("normal") street address matches within the provided postal code. In a sensitivity analysis, a very strict match criterion reduced the single-stage match rate to 67 percent, and reduced the proportion of discrepancy-distances above one kilometer to 0.5 percent (mean discrepancy-distance was reduced from 160 meters to 91 meters). Another sensitivity analysis showed that changing the offset from 25 to 30 feet in order to match the offset of the multi-stage method did not change any of the results substantially.

In order to explore the accuracy of the multi-stage and single-stage geocoding methods, we used Google Earth Pro (version 3.0, released November 17, 2005) to geocode a sample of the study addresses. This sample included all of the addresses matched by only one method and a random sample of 1000 addresses matched by both the single-stage and multi-stage geocoding methods. While not a perfect gold standard, Google Earth Pro incorporates information from satellite images during the relevant time period, 2003 to 2005. Supplemental geocoding results were similar to both multi-stage and single-stage geocoding results for addresses matched by both of these methods (Table 4 ). For the small number of addresses originally geocoded by the single-stage method but not the multi-stage method, supplemental geocoding did not correspond as closely to the single-stage result.


Google Earth uses two file formats: KML and KMZ. A KMZ file is a compressed Keyhole Markup Language (KML) file used to store location information, text links and other data used in the Google Earth mapping application. KML and KMZ files are essential the same, except that the KMZ is a compressed version of a KML. Open Google Earth. Open the KMZ file “Getting Started” in Google Earth. You can use one of the following methods to open the KMZ file: · Click directly on the KMZ file or · Select from the menu options File > Open, and browse your drive for the KML or KMZ file to open or · Open Google Earth, and use the shortcut “CTRL + O” (using a PC) or Command key ( ) + O (using a Mac), search for the file, and open it. If you have a MAC computer, please see the next section. For Mac OS Users If you have a PC, you can skip this section The Mac operating system (OS) associates file extensions, such as DOCX or TXT or HTML, to specific programs. The KMZ files on your Mac computer might not associate to Google Earth. To associate KMZ files to Google Earth: 1. Go to the Mac Finder application and select a KMZ file. 2. Go to the menu bar and then click Perform tasks with the selected item. 3. In the menu, select Get Info. Figure 1. Get Info option 4. In the Info window, click Open with. 5. Select Google Earth. 6. Click Change All. This action changes all KMZ files to open in Google Earth. Figure 2. Menu select options 7. Click Continue. This action confirms that you want all KMZ files to open in Google Earth. Figure 3. Continue option Structure of Modules This manual is a compilation of 20 stand-alone modules covering various sub-fields and topics in physical geography. While the content in each module differs, the structure among them is similar. Each module contains two components: · The first component is the lab component, which includes the lab instructions, topic content and questions. · The second component is the Google Earth KMZ file that you will download and then open in Google Earth. For all of the modules, Google Earth is an integral component, as you will use it to learn physical geography. All modules (with the exception of this module) have the following structure: · A list of key terms and concepts. · A series of measurable learning objectives you should acquire after you complete the material. · A series of vignettes that introduce you many of the main topics in the module, and include hyperlinks to videos and websites. · A physical geography topic from a global perspective. · Two to four sections that explore relevant themes and content in more detail. Each module contains approximately 40 questions, and takes approximately 1.5-2 hours to complete. INTRODUCTION In this module, we will cover many of the common Google Earth functions used in any physical geography lab module. Information regarding these and other functions can be found at the Getting to Know Google Earth website at the following URL: https://support.google.com/earth/answer/148176?hl=en&ref_topic=4380577 You will learn the following functions and terminology in this module: Table 2. Functions and terminology LAYOUT · 3D Viewer · Toolbar · Folders · Layers panel · Navigation controls · Opacity · Places panel · Search · Tour FILE MENU KML and KMZ File Formats EDIT MENU · Elevation Profile VIEW MENU · Grid · Historical Imagery · Sidebar · Status bar · Scale Legend TOOLS MENU · Elevation Exaggeration · Latitude Longitude · Ruler LAYOUT 3D Viewer The 3D viewer is the main viewing pane in Google Earth, and contains the Google Earth imagery (the globe). Viewing preferences – zooming in and out, imagery shown, 3D terrain, kmz files activated, and so on – affect what displays in the 3D Viewer. Surrounding the 3D Viewer are the Sidebar (to the left), the Toolbar (above the Viewer), and the navigation tools (to the right within the Viewer). Additional information such as scale, historical imagery, and coordinate data appear within the 3D Viewer space when activated. Figure 4. Google Earth 3D Viewer. Google and the Google logo are registered trademarks of Google Inc., used with permission. Sidebar The Sidebar is the main pane for all activities in the Google Earth labs. Specifically, the Sidebar displays the Search, Places, and Layers panes on the left side of the Google Earth application window. Figure 5. Google Earth Sidebar. Google and the Google logo are registered trademarks of Google Inc., used with permission. To show and hide the sidebar click View > Sidebar, or press Control + Alt + B, or click the Sidebar button ( ) on the toolbar. Mac users press Alt + + B. Follow the directions to show and hide the sidebar. QUESTION 1 : What happens to the earth when you turn off the sidebar? A. The Earth rotates B. The Earth remains in the same position C. The Earth moves you to your current location D. The Earth disappears Places Panel The Places panel enables you to manage what displays in the 3D viewer. It is the middle panel in the sidebar window and can be collapsed. This is the primary panel that you will use in this course when you navigate Google Earth, and where the KMZ files are found. If you have not yet done so, open the KMZ file “Getting Started” so that it shows in the Places panel in the Sidebar. When a file is first opened, it will be located under Temporary Places. If you close or exit out of Google Earth, you will be prompted to save the file. If you save the KMZ, it will show up next time under My Place in the Places panel. Figure 6. Save to My Places option. Google and the Google logo are registered trademarks of Google Inc., used with permission. In addition to saving a KMZ, you can remove or delete it. To delete a KMZ, click Edit > Delete, or highlight the KMZ file and press Delete . Figure 7. Delete KMZ. Google and the Google logo are registered trademarks of Google Inc., used with permission. Expand GETTING STARTED and then LAYOUT. Click Places Panel. Read the description and view the how-to video. Question 2 : How do you expand or collapse the Places panel? A. Right click on panel and choose expand/collapse B. Double click on the panel C. You cannot expand/collapse the panel D. Click on the blue triangle beside the word “Places” Uncheck the Places Panel folder. Folders The KMZ file contains various folders and files. Folders in Google Earth are similar to folders in most file management graphic user interface (GUI) environments. Specifically, there are parent folders and child folders. Parent folders contain child folders. Both parent and child folders can have check boxes or radio buttons to make the folder active. Check boxes enable you to select any or all of the child folders. With radio buttons, you can select only one child folder at a time. Click on the triangle to expand or retract the folders. Links are blue and underlined. Links open windows, animations, and web pages in the 3D Viewer window or web browsers. For each parent folder, the scale in which you see the imagery is correct (that is to say, there is no reason to zoom in or out unless otherwise noted). There will be times where you will be prompted to double-click a folder to zoom the imagery to the correct scale within the 3D Viewer window. Double‑click and select FOLDERS. Read the description and view the how-to video. QUESTION 3 : Which of the following is true? A. Parent folders must contain child folders B. Child folders can exist without parent folder C. Radio buttons allow for more than one parent folder to be active concurrently D. Check boxes allow for more than one child folder to be active concurrently Uncheck the Folders folder. Search The Search panel is located above the Places panel in the sidebar. While the Search panel and the Places panel allow for search functions in Google Earth, the Search panel enables you to type in and search for a specific address or location. The Search panel accepts the following syntax: o Place name (e.g. Eiffel Tower, Yosemite) o Organization name (e.g. Wiley Publishing) o Address (Street, City, State, Country) o Zipcode or Postal Code o Latitude/Longitude or UTM coordinate systems Double-click and select Search. Read the description and view the how-to video. Question 4: Type “Wiley Publishing” in the Search panel. Which of the following is the correct address? A. 111 River Street Hoboken, NJ B. 405 Lexington Avenue, NY, NY C. 1145 17th Street NW, Washington, DC D. 1600 Amphitheatre Parkway, Mountain View, CA Google Earth saves recent search terms. To clear your search history, click History > Clear History (Note: You might have to scroll to the bottom of the list if you have several searches), or click on the X found on the bottom right hand side of the Search panel. Expand Search Panel and then expand Now you try it – Search panel and follow the steps. Expand Now you try it – Places panel and follow the steps. Note : For long names within a KMZ file, it might be necessary to expand the width of the Sidebar by placing the cursor between the Sidebar and the 3D Viewer and moving the Sidebar toward the 3D Viewer. QUESTION 5 : Which of the following is true? (Check all that apply) A. You can search for a city by entering latitude and longitude B. You can search for a location by entering its name C. You can search for any word within a given KMZ file D. There are two Search functions in Google Earth Collapse and uncheck the Search Panel folder. Layers Panel The Layers panel is located below the Places panel in the Sidebar. The Layers panel enables you to show and hide geographic information provided by various agencies and other resources. Example layers include roads, weather, national parks, photos, map imagery supporting various global issues, and more. Double‑click and select Layers Panel. Read the description and view the how-to video. QUESTION 6 : Which folder has Volcanoes data? A. Weather B. Gallery C. Global Awareness D. More Expand Layers Panel and then and expand Now you try it. Make sure the check boxes are selected in order to zoom to the marked location. QUESTION 7 : What is the principal city west of the marked location? A. Rome B. Milan C. Italy D. Corse Collapse and uncheck the Layers Panel folder. Toolbar At the top of the Google Earth 3D Viewer is the Toolbar. The Toolbar enables you to use to buttons to engage and disengage functions that you can also find in the drop down menus. Consider these buttons as shortcuts to popular functions in Google Earth. The following list identifies the Toolbar icons and their menu equivalents: Table 3. Toolbar Icons Icon Description Menu equivalent Show or hide the sidebar View > Sidebar Display sunlight View > Sun Add aplacemark Add > Placemark Display sky, moon, and planets View > Explore > Earth, Sky, Mars, Moon Add a polygon Add > Polygon Measure tool Tools > Ruler Add a path Add > Path Email File > Email > Placemark, View, Image Add an image overlay Add > Image Overlay Print File > Print Record a tour Add > Tour Display the view in Google Maps File > View in Google Maps H istorical imagery View > Historical Imagery Double‑click and select TOOLBAR. Read the description and view the how-to video. QUESTION 8 : Which of the following icons allows you to display the location of day (daylight) and night (no daylight) at any given time on Earth? A. B. C. D. Uncheck the Toolbar folder. Navigation Controls The navigation controls, located in the top right of the 3D Viewer, enable you to pan, tilt, and zoom, the map imagery. The controls display fully when you move your mouse over them. Note: If your navigation controls are hidden, select View > Show Navigation > Automatically from the menu options. Double‑click and select Navigation Controls. Read the description and view the how-to video. Expand Navigation Controls and then expand Now you try it and follow the steps. QUESTION 9: Which function does not have a navigation control icon? A. Move around (pan) B. Look around C. Tilt D. Zoom Collapse and uncheck the Navigation Controls folder. Keyboard Shortcuts You can move the Earth and its imagery around in the 3D Viewer environment. As you become more familiar with Google Earth, you may wish to use keyboard shortcuts, particularly for navigation. Double‑click and select Keyboard Shortcuts. The table provides some common keystrokes for various navigation tasks. Table 4. Keyboard Shortcuts Action PC Keystroke Mac Keystroke Move to the left. Left arrow Left arrow Move to the right. Right arrow Right arrow Move up. Up arrow Up arrow Move down. Down arrow Down arrow Rotate the Earth clockwise Shift + left arrow Shift + left arrow Rotate the Earth counterclockwise Shift + right arrow Shift + right arrow Tilt the viewer to the horizon Shift + left mouse button + drag down Shift + down arrow Tilt the viewer to the top‑down view Shift + left mouse button + drag up Shift + up arrow Zoom in Scroll wheel, + key, or PgUp Scroll wheel, or + key Zoom out Scroll wheel, – key (both keyboard and numpad), or PgDn Scroll wheel, or – key (both keyboard and numpad) Display or close overview window. CTRL + M + M QUESTION 10 : Which two are true? A. To move the globe toward the West, press W on the keyboard B. To move the globe toward the East, press E on the keyboard C. To move the globe toward the South, press S on the keyboard D. To move the globe toward a North orientation, press N on the keyboard QUESTION 11 : What happens if you press the letter “r”? A. Open the Help menu B. Place latitude and longitude grid over the viewer C. Reset the view to North and the tilt angle top down D. Bring the image to the default zoom of the entire Earth Uncheck Keyboard Shortcuts folder. OPACITY Within the Panels layer is the Opacity icon and slider. The Opacity function enables you to set the transparency of images in the 3D viewer. The icon and slider set at the bottom of the Places panel. Double‑click and select Opacity. Read the description and view the how-to video. Expand Opacity and then expand Now you try it and follow the steps. Expand Now you try it and answer the following questions: Question 12: What do you see when you slide the Adjust Opacity to the far left? A. The Google Earth image B. The thematic map overlay C. No change D. All images disappear Question 13: Where would you place the Adjust Opacity slider to see both the Google Earth image and the thematic map? A. All the way to the right B. All the way to the left C. Turn off the opacity function D. Approximately in the middle Collapse and uncheck the Opacity folder. Collapse and uncheck the LAYOUT folder. MENU OPTIONS File, Edit, View, Tools, Add, and Help are the six menus found along the top of Google Earth. Within these menu options are several functions used in the lab modules. While many of these functions have more than one pathway (that is to say, there is usually a menu option and a shortcut option), the following are a list of functions used in the lab modules, with the predominant menus noted. Table 5. Menu Options Function Menus Elevation Profile Edit, Tools Status Bar View Grid View Scale Legend View Historical Imagery View Ruler Tools Elevation Exaggeration Tools Latitude and Longitude Tools Units of Measurement Tools Tour Add Elevation Profile Paths in Google Earth have an elevation characteristic which you can view in a cross‑section format. The elevation profile shows you a cross section of the topography and computes the distance, relief, and slope of the profile line. You can move the cursor along the elevation profile to see the slope of the line at a given location. To view the elevation profile: · Select the path line item in the Places panel and then click Edit > Show Elevation Profile, or · Right‑click the path line item in the Places panel and then click Show Elevation Profile Google Earth displays the profile at the bottom of the 3D viewer. To close the elevation profile, click the X in the top right corner of the profile window. Expand MENU OPTIONS and then double‑click and select Elevation Profile. Read the description and view the how-to video. Note: To answer questions, you will have to know how to change the units, if necessary. Sometimes questions will ask for British units (for example, miles and feet) and sometimes questions will ask for metric units (for example, kilometers and meters). To change the Units of Measurement: 1. Click Tools >Options 2. Go to the 3D View tab and change the measurement to Feet, Miles, or Meters, Kilometers 3. Choose Feet, Miles for this portion of the lab, and then click OK. Expand Elevation Profile and then expand Now you try it and follow the steps. Question 14: What is the approximate length the profile (choose the closest number and unit)? A. Approximately 243 miles B. Approximately 246 km C. Approximately 312 meters D. Approximately 514 feet Question 15: What is the maximum positive slope? A. 2.1% B. 4.1% C. 7.8% D. 29.3% Question 16: What is the maximum elevation? A. 243 feet B. 3815 feet C. 11861 feet D. 12323 feet Collapse and uncheck the Elevation Profile folder. Status Bar The status bar enables you to view coordinates and elevation of your cursor location. In addition, the imagery date and streaming status are provided. These data are displayed at the bottom center in the 3D Viewer. To display the status bar, click View > Status Bar. Double‑click and select Status Bar. Read the description and view the how-to video. Expand Status Bar and then expand Now you try it and follow the steps. Question 17 : Zoom out until the outline of the globe is visible. What happens to the coordinates if you pan off the globe and into space? A. There are no coordinates shown B. There are negative coordinates shown C. There is an error message D. The coordinates shown equal zero Collapse and uncheck the Status Bar folder. Grid The grid feature displays or turns off the coordinate system you are using (for example, latitude and longitude) in the 3D Viewer. To display the grid: · Click View > Grid, or · Press Control + L. Mac user press + L . (Because this is known as a toggle, press this key combination repeatedly to engage or to disengage the function.) Double‑click and select Grid. Read the description and view the how-to video. Expand Grid and then expand Now you try it and follow the steps. Question 18 : How do you turn off the grid? (Check all that apply) A. Press Ctrl + L on a PC B. Press Esc on a PC C. Press + L on a Mac D. Press the undo button Collapse and uncheck the Grid folder. Scale Legend The scale legend displays in the 3D Viewer and continually updates as you move around, and zoom in or out. You can configure the units of measurement in the scale. To display the scale legend, click View > Scale Legend. The legend displays in the bottom left corner in the 3D viewer. Double‑click and select Scale Legend. Read the description and view the how-to video. Change the Units of Measurement under Tools >Options to Meters, Kilometers in the 3D View. Then click OK. Expand Scale Legend and then expand Now you try it and follow the steps. Question 19: Approximately how many meters is the distance represented by the scale bar? A. 1400 meters B. 400 meters C. 120 meters D. 1250 meters Collapse and uncheck the Scale Legend folder. Historical Imagery The historical imagery function enables you to view historical imagery for a given location. Specifically, you can view the change of features, both natural and manmade, over time and space. To use historical imagery: · Click the Show historical imagery button () on the toolbar, or 1. Click View > Historical Imagery. 2. Use the slider to view images from multiple acquisition dates. Some location images go back prior to satellite technology for example, Las Vegas from 1950. For fun, do a Google search on historical imagery to see what other Google Earth users have discovered. Double‑click and select Historical Imagery. Read the description and view the how-to video. Expand Historical Imagery and then expand Now you try it and follow the steps. Question 20: What significant land-uses changes did you observe? A. Golf course added B. Agriculture increased C. Road network increased D. Human settlement increased Collapse and uncheck the Historical Imagery folder. If still active, click the X in the top right corner of the Historical Imagery slider in the 3D Viewer. Ruler The ruler enables you to measure length of a line or a path. A line is a distance between two points, while a path is a measurement of distance of multiple points connected by straight lines. To use the ruler: 1. Click Tools > Ruler, or click Show Ruler () on the toolbar. 2. Select the tab for the shape that you will measure (Line, Path). 3. Select the units you want to use (for example, feet, meters, miles). 4. Go to the 3D viewer and begin measuring. As you draw, your results display in the Ruler window. 5. Click Save if you want to save the line/path you created. Double‑click and select RULER. Read the description and view the how-to video. Expand Now you try it – line and follow the steps. Question 21: What is the approximate length of the line (with the correct unit of measurement)? A. 205 miles B. 330 km C. 330 miles D. 205 km Expand Now you try it – path and follow the steps. Question 22: What is the approximate length of the path (with the correct unit of measurement)? A. 205 miles B. 330 km C. 330 miles D. 205 km Collapse and uncheck the Ruler folder. Elevation Exaggeration The elevation exaggeration function enables you to get a more pronounced view of natural features that have a noted elevation change (for example, mountains and canyons). To set the exaggeration level: 1. Click Tools > Options. Mac users click Google Earth > Preferences. 2. In the Google Earth Options window, click the 3D View tab. 3. Enter a value in the Elevation Exaggeration field. Note: The lower the number, the smaller the vertical exaggeration. Conversely, the higher the number, the more pronounced the vertical exaggeration. Typically, 2 is best integer to use to show exaggeration for most labs (decimals are permitted). However, during tours or flyovers, you might want to set a lower exaggeration setting (0.5-1). Double‑click and select ELEVATION EXAGGERATION. Read the description and view the how-to video. Expand Now you try it and follow the steps. Question 23: What is the elevation exaggeration when you apply “Restore Defaults” in the 3D View tab? A. 0.5 B. 1 C. 2 D. 3 Question 24: When is the elevation exaggeration the greatest? A. 0.5 B. 1 C. 2 D. 3 Collapse and uncheck the Elevation Exaggeration folder. Latitude/Longitude Google Earth enables you to configure how latitude and longitude coordinates display at the bottom of the 3D Viewer. To configure latitude and longitude coordinates: 1. Verify that the status bar is enabled click View > Status Bar. 2. Click Tools > Options. Mac users click Google Earth > Preferences. 3. In the Google Earth Options window, click the 3D View tab. 4. In the Show Lat/Long section, select the option you want. The following table shows the latitude‑longitude types for Iceland: Table 6. Latitude Longitude Lat/Long Selection Result Decimal Degrees lat 64.963051° lon -19.020835° elev 0 ft Degrees, Minutes, Seconds 64°57󈧲.98″N 19°01󈧓.01″ W elev 0 ft Degrees, Decimal Minutes 64°57.783′ N 19° 1.250′ W elev 0 ft Universal Transverse Mercator 27 W 593447.78m E 7205799.22m N elev 0 ft Double‑click and select LATITUDE LONGITUDE. Read the description and view the how-to video Expand Now you try it and follow the steps. Question 25: Which Show Lat/Long selection displays measurements in meters? A. Decimal degrees B. Degrees, Minutes, Seconds C. Degrees, Decimal Minutes D. Universal Transverse Mercator Question 26: What are the latitude/longitude coordinates of the Central Park markers in Decimal degrees? A. 43. 77°N, 70.97°W B. 40. 77°S, 73.97°E C. 43. 77°S, 70.97°W D. 40. 77°N, 73.97°W Collapse and uncheck the Latitude/Longitude folder. Tour The tour function enables you to control the tours (flight simulations) of the Earth. The tour control panel appears when you start any of the tours in the course. The buttons are similar to any video or audio device with rewind, play, pause, and fast-forward controls. Note: Some Google Earth functions are not available when the tour control panel is open consequently, close the tour control panel after viewing a tour. Double‑click and select TOUR. Read the description and view the how-to video. Question 27: What icon appears in the folder when you are watching a tour A. Folder icon B. Dots with lines icon C. Video Camera Google Earth icon

Google Earth uses two file formats: KML and KMZ. A KMZ file is a compressed Keyhole Markup Language (KML) file used to store location information, text links and other data used in the Google Earth mapping application. KML and KMZ files are essential the same, except that the KMZ is a compressed version of a KML.

Open the KMZ file “Getting Started” in Google Earth.

You can use one of the following methods to open the KMZ file:

· Click directly on the KMZ file or

· Select from the menu options File > Open, and browse your drive for the KML or KMZ file to open or

· Open Google Earth, and use the shortcut “CTRL + O” (using a PC) or Command key ( ) + O (using a Mac), search for the file, and open it.

If you have a MAC computer, please see the next section.

If you have a PC, you can skip this section

The Mac operating system (OS) associates file extensions, such as DOCX or TXT or HTML, to specific programs. The KMZ files on your Mac computer might not associate to Google Earth.

To associate KMZ files to Google Earth:

1. Go to the Mac Finder application and select a KMZ file.

2. Go to the menu bar and then click Perform tasks with the selected item.

3. In the menu, select Get Info. Figure 1. Get Info option

4. In the Info window, click Open with.

6. Click Change All. This action changes all KMZ files to open in Google Earth. Figure 2. Menu select options

7. Click Continue. This action confirms that you want all KMZ files to open in Google Earth. Figure 3. Continue option

This manual is a compilation of 20 stand-alone modules covering various sub-fields and topics in physical geography. While the content in each module differs, the structure among them is similar.

Each module contains two components:

· The first component is the lab component, which includes the lab instructions, topic content and questions.

· The second component is the Google Earth KMZ file that you will download and then open in Google Earth. For all of the modules, Google Earth is an integral component, as you will use it to learn physical geography.

All modules (with the exception of this module) have the following structure:

· A list of key terms and concepts.

· A series of measurable learning objectives you should acquire after you complete the material.

· A series of vignettes that introduce you many of the main topics in the module, and include hyperlinks to videos and websites.

· A physical geography topic from a global perspective.

· Two to four sections that explore relevant themes and content in more detail.

Each module contains approximately 40 questions, and takes approximately 1.5-2 hours to complete.


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text conference publication ws-2014-special 10.3115/v1/W14-43 https://www.aclweb.org/anthology/W14-4300 Keynote: Statistical Approaches to Open-domain Spoken Dialogue Systems Steve Young author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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Out-of-Domain Spoken Dialogs in the Car: A WoZ Study Sven Reichel author Jasmin Sohn author Ute Ehrlich author André Berton author Michael Weber author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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Situated Language Understanding at 25 Miles per Hour Teruhisa Misu author Antoine Raux author Rakesh Gupta author Ian Lane author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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Information Navigation System Based on POMDP that Tracks User Focus Koichiro Yoshino author Tatsuya Kawahara author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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conference publication yoshino-kawahara-2014-information 10.3115/v1/W14-4305 https://www.aclweb.org/anthology/W14-4305

Adapting to Personality Over Time: Examining the Effectiveness of Dialogue Policy Progressions in Task-Oriented Interaction Alexandria Vail author Kristy Boyer author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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Probabilistic Human-Computer Trust Handling Florian Nothdurft author Felix Richter author Wolfgang Minker author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

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Learning non-cooperative dialogue behaviours Ioannis Efstathiou author Oliver Lemon author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication efstathiou-lemon-2014-learning 10.3115/v1/W14-4308 https://www.aclweb.org/anthology/W14-4308

Improving Classification-Based Natural Language Understanding with Non-Expert Annotation Fabrizio Morbini author Eric Forbell author Kenji Sagae author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication morbini-etal-2014-improving 10.3115/v1/W14-4309 https://www.aclweb.org/anthology/W14-4309

User Modeling by Using Bag-of-Behaviors for Building a Dialog System Sensitive to the Interlocutor’s Internal State Yuya Chiba author Masashi Ito author Takashi Nose author Akinori Ito author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication chiba-etal-2014-user 10.3115/v1/W14-4310 https://www.aclweb.org/anthology/W14-4310

Alex: Bootstrapping a Spoken Dialogue System for a New Domain by Real Users Ondřej Dušek author Ondřej Plátek author Lukáš Žilka author Filip Jurčíček author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication dusek-etal-2014-alex 10.3115/v1/W14-4311 https://www.aclweb.org/anthology/W14-4311

InproTKs: A Toolkit for Incremental Situated Processing Casey Kennington author Spyros Kousidis author David Schlangen author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication kennington-etal-2014-inprotks 10.3115/v1/W14-4312 https://www.aclweb.org/anthology/W14-4312

Back to the Blocks World: Learning New Actions through Situated Human-Robot Dialogue Lanbo She author Shaohua Yang author Yu Cheng author Yunyi Jia author Joyce Chai author Ning Xi author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication she-etal-2014-back 10.3115/v1/W14-4313 https://www.aclweb.org/anthology/W14-4313

An easy method to make dialogue systems incremental Hatim Khouzaimi author Romain Laroche author Fabrice Lefevre author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication khouzaimi-etal-2014-easy 10.3115/v1/W14-4314 https://www.aclweb.org/anthology/W14-4314

Free on-line speech recogniser based on Kaldi ASR toolkit producing word posterior lattices Ondřej Plátek author Filip Jurčíček author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication platek-jurcicek-2014-free 10.3115/v1/W14-4315 https://www.aclweb.org/anthology/W14-4315

Combining Task and Dialogue Streams in Unsupervised Dialogue Act Models Aysu Ezen-Can author Kristy Boyer author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication ezen-can-boyer-2014-combining 10.3115/v1/W14-4316 https://www.aclweb.org/anthology/W14-4316

Dialogue Act Modeling for Non-Visual Web Access Vikas Ashok author Yevgen Borodin author Svetlana Stoyanchev author IV Ramakrishnan author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication ashok-etal-2014-dialogue 10.3115/v1/W14-4317 https://www.aclweb.org/anthology/W14-4317

Extractive Summarization and Dialogue Act Modeling on Email Threads: An Integrated Probabilistic Approach Tatsuro Oya author Giuseppe Carenini author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication oya-carenini-2014-extractive 10.3115/v1/W14-4318 https://www.aclweb.org/anthology/W14-4318

Keynote: Language Adaptation Lillian Lee author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication lee-2014-keynote 10.3115/v1/W14-4319 https://www.aclweb.org/anthology/W14-4319

Addressing Class Imbalance for Improved Recognition of Implicit Discourse Relations Junyi Jessy Li author Ani Nenkova author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication li-nenkova-2014-addressing 10.3115/v1/W14-4320 https://www.aclweb.org/anthology/W14-4320

The Role of Polarity in Inferring Acceptance and Rejection in Dialogue Julian Schlöder author Raquel Fernández author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication schloder-fernandez-2014-role 10.3115/v1/W14-4321 https://www.aclweb.org/anthology/W14-4321

In-depth Exploitation of Noun and Verb Semantics to Identify Causation in Verb-Noun Pairs Mehwish Riaz author Roxana Girju author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication riaz-girju-2014-depth 10.3115/v1/W14-4322 https://www.aclweb.org/anthology/W14-4322

Identifying Narrative Clause Types in Personal Stories Reid Swanson author Elahe Rahimtoroghi author Thomas Corcoran author Marilyn Walker author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication swanson-etal-2014-identifying 10.3115/v1/W14-4323 https://www.aclweb.org/anthology/W14-4323

Evaluating a Spoken Dialogue System that Detects and Adapts to User Affective States Diane Litman author Katherine Forbes-Riley author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication litman-forbes-riley-2014-evaluating 10.3115/v1/W14-4324 https://www.aclweb.org/anthology/W14-4324

Initiative Taking in Negotiation Elnaz Nouri author David Traum author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication nouri-traum-2014-initiative 10.3115/v1/W14-4325 https://www.aclweb.org/anthology/W14-4325

Knowledge Acquisition Strategies for Goal-Oriented Dialog Systems Aasish Pappu author Alexander Rudnicky author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication pappu-rudnicky-2014-knowledge 10.3115/v1/W14-4326 https://www.aclweb.org/anthology/W14-4326

Reducing Sparsity Improves the Recognition of Implicit Discourse Relations Junyi Jessy Li author Ani Nenkova author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication li-nenkova-2014-reducing 10.3115/v1/W14-4327 https://www.aclweb.org/anthology/W14-4327

Interaction Quality Estimation in Spoken Dialogue Systems Using Hybrid-HMMs Stefan Ultes author Wolfgang Minker author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication ultes-minker-2014-interaction 10.3115/v1/W14-4328 https://www.aclweb.org/anthology/W14-4328

Learning to Re-rank for Interactive Problem Resolution and Query Refinement Rashmi Gangadharaiah author Balakrishnan Narayanaswamy author Charles Elkan author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication gangadharaiah-etal-2014-learning 10.3115/v1/W14-4329 https://www.aclweb.org/anthology/W14-4329

Aspectual Properties of Conversational Activities Rebecca J Passonneau author Boxuan Guan author Cho Ho Yeung author Yuan Du author Emma Conner author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication passonneau-etal-2014-aspectual 10.3115/v1/W14-4330 https://www.aclweb.org/anthology/W14-4330

Detecting Inappropriate Clarification Requests in Spoken Dialogue Systems Alex Liu author Rose Sloan author Mei-Vern Then author Svetlana Stoyanchev author Julia Hirschberg author Elizabeth Shriberg author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication liu-etal-2014-detecting 10.3115/v1/W14-4331 https://www.aclweb.org/anthology/W14-4331

Using Ellipsis Detection and Word Similarity for Transformation of Spoken Language into Grammatically Valid Sentences Manuel Giuliani author Thomas Marschall author Amy Isard author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication giuliani-etal-2014-using 10.3115/v1/W14-4332 https://www.aclweb.org/anthology/W14-4332

SAWDUST: a Semi-Automated Wizard Dialogue Utterance Selection Tool for domain-independent large-domain dialogue Sudeep Gandhe author David Traum author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication gandhe-traum-2014-sawdust 10.3115/v1/W14-4333 https://www.aclweb.org/anthology/W14-4333

A Demonstration of Dialogue Processing in SimSensei Kiosk Fabrizio Morbini author David DeVault author Kallirroi Georgila author Ron Artstein author David Traum author Louis-Philippe Morency author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication morbini-etal-2014-demonstration 10.3115/v1/W14-4334 https://www.aclweb.org/anthology/W14-4334

MVA: The Multimodal Virtual Assistant Michael Johnston author John Chen author Patrick Ehlen author Hyuckchul Jung author Jay Lieske author Aarthi Reddy author Ethan Selfridge author Svetlana Stoyanchev author Brant Vasilieff author Jay Wilpon author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication johnston-etal-2014-mva 10.3115/v1/W14-4335 https://www.aclweb.org/anthology/W14-4335

The PARLANCE mobile application for interactive search in English and Mandarin Helen Hastie author Marie-Aude Aufaure author Panos Alexopoulos author Hugues Bouchard author Catherine Breslin author Heriberto Cuayáhuitl author Nina Dethlefs author Milica Gašić author James Henderson author Oliver Lemon author Xingkun Liu author Peter Mika author Nesrine Ben Mustapha author Tim Potter author Verena Rieser author Blaise Thomson author Pirros Tsiakoulis author Yves Vanrompay author Boris Villazon-Terrazas author Majid Yazdani author Steve Young author Yanchao Yu author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication hastie-etal-2014-parlance 10.3115/v1/W14-4336 https://www.aclweb.org/anthology/W14-4336

The Second Dialog State Tracking Challenge Matthew Henderson author Blaise Thomson author Jason D Williams author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication henderson-etal-2014-second 10.3115/v1/W14-4337 https://www.aclweb.org/anthology/W14-4337

Optimizing Generative Dialog State Tracker via Cascading Gradient Descent Byung-Jun Lee author Woosang Lim author Daejoong Kim author Kee-Eung Kim author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication lee-etal-2014-optimizing 10.3115/v1/W14-4338 https://www.aclweb.org/anthology/W14-4338

Web-style ranking and SLU combination for dialog state tracking Jason D Williams author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication williams-2014-web 10.3115/v1/W14-4339 https://www.aclweb.org/anthology/W14-4339

Word-Based Dialog State Tracking with Recurrent Neural Networks Matthew Henderson author Blaise Thomson author Steve Young author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication henderson-etal-2014-word 10.3115/v1/W14-4340 https://www.aclweb.org/anthology/W14-4340

Comparative Error Analysis of Dialog State Tracking Ronnie Smith author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication smith-2014-comparative 10.3115/v1/W14-4341 https://www.aclweb.org/anthology/W14-4341

Extrinsic Evaluation of Dialog State Tracking and Predictive Metrics for Dialog Policy Optimization Sungjin Lee author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication lee-2014-extrinsic 10.3115/v1/W14-4342 https://www.aclweb.org/anthology/W14-4342

The SJTU System for Dialog State Tracking Challenge 2 Kai Sun author Lu Chen author Su Zhu author Kai Yu author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication sun-etal-2014-sjtu 10.3115/v1/W14-4343 https://www.aclweb.org/anthology/W14-4343

Markovian Discriminative Modeling for Dialog State Tracking Hang Ren author Weiqun Xu author Yonghong Yan author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)

Association for Computational Linguistics

conference publication ren-etal-2014-markovian 10.3115/v1/W14-4344 https://www.aclweb.org/anthology/W14-4344

Sequential Labeling for Tracking Dynamic Dialog States Seokhwan Kim author Rafael E Banchs author 2014-jun text Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)