Attention Community Map Contributors!

• Web-based Applications—GIS applications delivered via web browser
• Mobile GIS Applications—GIS applications developed for mobile platforms such as Windows Phone, Android, iOS (iPad or iPhone), ArcGIS Mobile, or ArcPad
• Desktop GIS Applications—ArcGIS Desktop or ArcEngine applications, including entries that are developed using Visual Basic for Applications (VBA), ArcObjects SDK (Java, C++, .NET), or Python
• Multimedia Maps (Former Virtual Map Gallery)—Animations, CD/DVD ROM, or other multimedia presentations displaying the results of GIS analysis

The Community Maps Team will be looking for applications that incorporate Community Maps use. Over the past few years, many of the contributors to Community Maps have been creating some pretty slick web applications! We are challenging our contributors to put their best app forward and submit an entry. Remember to reference my last Blog: Best Practices for Building and Sharing Community Web Maps and Apps for tips on what to include with your application submission.

We look forward to viewing your submission and we’ll see you at the conference!

Debate about how best to represent election results is nothing new, fuelled principally by the default two-colour map commonly used to show red vs blue States (Figure 1).

Figure 1: 2012 US Election result at state level as a unique values map (source: Mark Newman 2012)

You only have to look at the map and ask the question ‘who won the election?’ to see the problem.  There is more red than blue which suggests a win for the Republicans when the opposite was the case. The electoral college voting system means that a state will be mapped as either red or blue.  Large states become visually dominant and while they might not contribute many electoral college votes to the total they appear visually prominent. The other problem here is that the different sized areas alters the visual prominence of some States relative to others, regardless of how many people or voters live there. The data should be normalized to take account of the population distribution because it doesn’t allow for the fact that the large red states generally have smaller populations.  At least Figure 1 used an Albers equal area conic projection…the key part being ‘equal area’ so it ensures that each of the states is in its correct proportion compared to all the others.

Figure 2: 2012 US Election results mapped using different techniques (sources: Newman, 2012; Axis Maps, 2012; Nelson, 2012)

The use of a Mercator projection (or Web Mercator on a web map) further distorts the areas of the states and compounds the visual problems seen in Figure 1 (Figure 2a). Figure 2b shows how at County level, the red/blue approach seems to magnify the wrong visual message even further. A population density equalizing cartogram can be used to modify the sizes of the states by rescaling according to the number of electoral votes (Figure 2c). This accommodates the visual problem of red dominating the map but modifies the map shape and can cause difficulty for readers unfamiliar with this map type.

Figure 2d shows the percentage of vote with vivid red showing areas of strong Republican support, vivid blues showing strong Democrat support and shades of purple for areas with a strong balance between voting. Because the map shows percentages of votes by total vote, the data is normalised using a consistent denominator and the map becomes a true choropleth allowing for visual comparisons to be accurately made across the map. The value by alpha approach is shown in Figure 2e which uses saturation of colour so areas of low population fade to black. A dot density approach is illustrated in Figure 2f which goes some way to showing the mix of vote per county.  Each blue or red dot represents 1,000 votes. Density of voter turnout is reflected in the different dot densities seen in each county.  If we consider voter turnout a proxy for population then we can see where the more densely populated counties are and vice versa.  We can also see counties with predominantly blue or red dots and those where the dots are mixed (giving rise to the illusion of purple hues because of the close proximity of dots), showing a more balanced vote between the candidates.

However, a drawback of all the above examples is that they use States or Counties as a basic unit in which to represent the data visually. With much of the US unpopulated, large Counties with sparse populations are not easily compared to small counties with densely populated areas and vice versa.

An alternative approach is to create a dasymetric map; a map popularised by the American geographer John Kirtland Wright in the 1940s (who incidentally also coined the term ‘choropleth’ in 1938). A dasymetric map is a type of thematic map that attempts to correct the errors we’ve seen that result from mapping population data using choropleths.  It is a form of spatial classification of data where ancillary data is used to reapportion data captured in one geographic area to another geographic area.  In terms of our map of election votes, if we think of a county as the area used as the original container for the data, a better way to map the data might be to display it using boundaries that reflect the distribution of the population within that area.  This overcomes the visual assumption of choropleths that the population is evenly distributed within each area.

Take, for example, San Bernardino County in California.  The county covers approximately 20,000 sq miles in Southern California yet the proportion of urbanised area is extremely small. These areas are where virtually all of the 450,000 voters live (Figure 3a).

Figure 3. San Bernardino county in Southern California

The entire area would be coloured blue in the traditional red/blue map since the Democrats won the County with 51% of the vote…a narrow margin (Figure 3b). If we used a dot density approach (with 1 dot = 200 votes) then the voting pattern would be distributed throughout the sparsely populated County which contrasts with the adjacent Los Angeles County and Orange County (Figure 3c). However, a dasymetric map reapportions the data into the populated areas. In Figure 3d, the vote count data has been reapportioned into different urban categories to reflect high density, low density and non-urban (inhabited) areas. Certain other land uses such as airports and parks are masked out to avoid areas where we know people do not live and the data rendered as a dot density. This approach has several advantages over the alternatives namely:

• the arbitrary County boundaries no longer give abrupt ‘data cliffs’ between adjacent areas;
• data is no longer distributed randomly and evenly within an area;
• data is now more sensibly distributed according to the object density of the real distribution;
• densities of dots can be compared based on a uniform object density mapping;
• the density of one city can be compared to another more easily;
• the shape of the map becomes more recognisable since we can more easily relate to real places; and
• the map overcomes the problem of false interpretation based on non-normalised data densities.

Figure 4 shows the dasymetric dot density map for the contiguous United States at 1:18million and the web map can be viewed at ArcGIS Online.

Figure 4. Dasymetric dot density map for the continuous United States at 1:18million

So how did we create a dasymetric dot density map using ArcGIS?

We used a new suite of Area Weighting Models available for download from ArcGIS Online.  The tools implement three different methods for moving data between different geographies.  In the case we here, we used the mask area weighting tool.  Full details of how the tools work are included in the documentation for the tools.

The source zones we used are the US counties feature class with the vote count data held as attributes in two fields (Democrat and Republican).  The target zones are polygonal feature classes derived from the USGS 30m raster National Land Cover Dataset.  We processed the NLCD to extract the high density urban areas, low and medium density urban areas and nonurban inhabited areas as well as a background mask (all other land cover types plus a mask of known non-populated urban areas such as airports and parks).  The raster datasets were converted to polygon feature classes and we ran the mask area weighting tool to transfer our votes to the new features.  We then used the Field Calculator to weight the votes in each of the three target zone feature classes so that 60% of votes were allocated to the high density polygons, 30% to the low density polygons and 10% to the non-urban (inhabited) polygons. Using the dot density renderer we then mapped the two fields, symbolising republican votes as red dots and democrat as blue. We removed the outline and fill symbols for the areas to leave just the dots across our custom built dark grey basemap.

The dot density renderer requires you to determine a dot value and a dot size which is a function of the scale of your map and your data. Ideally you should seek a suitable balance between dot size and value to give a good distribution across your map making both the areas with sparse data densities visible as well as not overcrowding those areas with high densities.

The map isn’t finished though…

A single scale dasymetric dot density map will restrict us to a single dot density specification.  By using the multiscale web map pattern in ArcGIS Online we can create different versions of the map to suit different scales.  We created a multiscale web map from 1:18million through the ten larger scales to 1:36,000.  We changed the dot density of 1 dot = 1,000 votes at 1:18 million to 1 dot = 10 votes at 1:36,000 and built map services for each of the scales for the finished map.  This provides us with flexibility to be able to show overall patterns at a national and regional scale but which adds detail at each successively larger scale.

We maintained the Albers equal area projection online as described in a previous blog entry using alternative thematic basemaps with the ArcGIS.com map viewer to overcome the limitations of Web Mercator for this data type.  The detail of the basemap was also modified for each zoom level to ensure it was appropriate for each scale and supported the thematic detail effectively. We also added a popup at all but the smallest scale to allow users to mine the county data and reveal the vote data itself as well as some topographic detail to orient map users, particularly at larger scales.

Summary

The use of a dasymetric mapping technique supports the redistribution of thematic data from one geographic area to other areas using ancillary data.  The resulting map reflects a truer picture of population distribution that accounts for the large swathes of uninhabited land and shows the true distribution of voting patterns in the US.  It overcomes the limitations of the choropleth map and the dot density map that use standard geographic areas used for data aggregation.  It also provides a more realistic map image rather than having to resort to a cartogram approach to account for differences in the size of areas and the magnitude and distribution of the population within. We do have to be careful to ensure we don’t ascribe too much significance to the precise location of each dot though…they have been redistributed within new boundaries and do not reflect the actual position of a particular group of voters. We could have made a map that shows 1 dot = 1 vote but that would support some gross misinterpretations which are referred to as the ecological fallacy – something we should always try and avoid in mapping statistical data)

References

Axis Maps 2012. Shedding Light on Election Demographics – 2012, http://axismaps.com/election2012/

Nelson, J. 2012 Election 2012, UX Blog http://uxblog.idvsolutions.com/2012/11/election-2012.html

Newman, M. 2012 Maps of the 2012 US presidential election results, http://www-personal.umich.edu/~mejn/election/2012/

In addition to allowing for standardization of cartographic rules, visual specifications support the following workflows:

• Manage representations across many layers - Typically, creating and managing feature class representations must be done one layer at a time. And you might have to manage many layers to create your maps. Batch processing of all the layers in your map document is the most efficient way to handle this. Visual specifications allow you to effectively use representations across all layers in your map document, not just a few.
• Link feature attribution to representation rules - When standard symbols are converted to representations, the renderer settings used to identify what feature got what symbol is lost. Visual specifications allow you to pre-configure the logic to link feature attribution to the symbols. This means that the map’s representation rules are automatically updated when the feature attribution changes. So maps symbols can be truly data-driven and maintained over time even when the underlying feature’s attribution changes.
• Apply symbology using relational queries - The logic to link feature attribution to symbols within visual specifications is written using SQL – not just the where clause, but the whole SQL statement!  This allows labels and symbols to be updated from information found not only on the base table but also any table that can be related. This makes visual specifications very powerful, especially when you have a normalized data model.

There are different roles for creating and applying the visual specifications. Typically, an administrator configures the rules for the users to apply those specifications to the features during map production. Let’s discuss each role in more detail.

The Administrator

Visual Specifications can be accessed from the Production Symbology toolbar.

The administrator has several different tasks associated with creating, managing and designing the specifications such as specifying the workspace (geodatabase) that will store the visual specifications, adding a specifications description and most importantly defining what symbol gets assigned to what features based on its attribution, i.e. the specification rules.

The administrator can create these rules within the Layer Properties dialog box (if the Display Calculate Representations and Calculate Fields tabs option is checked) or in the advance dialog box.  Let’s look at configuring visual specifications within the Layer Properties.

When using Visual Specifications, you’ll see two new tabs added to the feature’s Layer Properties dialog box below. The Calculated Representations tab allows you to define the symbology while the Calculated Fields tab allows you to define labels.

How to create a rule

You can create a new calculate representation rule by clicking Options>> and choosing New Rules. Once a record is added you will need to define the following parameters:

• Rule Description – A description or name of the rule to help you identify what features are being symbolized
• Representation Name – Defines what feature class representation will be used
• SQL Statement – Defines the features you want to symbolize
• Expression –  Optional refinement beyond the SQL results for complex feature selection rules; uses VB script
• Representation Rule – The specific representation rule that will assigned based on the features selected using the SQL statement
• Explicit Override Fields – Defines what representation  properties will have their values read from fields on the feature class

You can create a new calculate fields rule by clicking options and choosing the New Rules option. Once a record is added you will need to define the following parameters:

• Rule Description – A description or name of the rule
• Field Name – Defines the field and its properties will be used to save the resulting value after it’s calculated
• SQL Statement – Defines the features you want to label
• Expression – Defines the fields and optional formatting of the string you want to use for labeling
• Preview – Preview the label in its entirety

Once the specification design process has been completed and tested the specifications are ready to be distributed and applied.

The User

Users, the ones who generate the map products, can apply these specifications using the Calculate Visual Specification geoprocessing tool located in the Symbology toolset.

The following parameters need to be provided when applying visual specification rules to feature classes, feature layer and/or table:

• Input table – Feature layers, feature classes or tables to which visual specificatons will be applied
• Visual specifications table location – Workspace that contains the visual specification
• Render Map  with Calculated Representations – updates the layer’s properties to show the representations
• Display Calculated Fields as labels  – updates the layer’s properties by creating label classes and enables the layer to draw labels for all fields that have had their values calculated.
• Preserve Free Representations – If a feature class has been manually edited to create free representations, their values/symbols will not be updated when the tool runs.

There are visual specification geoprocessing tools to Drop Visual Specifications and to Select Features by Specification Difference.  The Drop Visual Specifications removes the fields associated with the visual specification while Select Features by Specification Difference selects features  with representation rule IDs or calculated fields that do not match the visual specification.

Happy Mapping!

The demonstration that I gave at the 2013 AAG Annual Meeting has an associated PowerPoint presentation that you can now download. The Mapping Flow Data presentation is about 16 MB because it has videos.

Topics covered in the presentation include:

• What are flow maps?
• What types of flow maps are there?

The remainder of the presentation was a demonstration of how to make the various types of flow maps using ArcGIS. This is also a topic for an upcoming workshop at the 2013 Esri Education User Conference in July. After that conference I will make the workshop resources available for download here on our blog. Resources in the downloads will include the PowerPoint slides, Word documents for the exercises, and GIS data/maps. You can use these to learn the techniques yourself or teach others in your workplace or classroom.

Look for the workshop resources in July, and for now, hopefully this short PowerPoint presentation, Mapping Flow Data, will be useful to you!

Last weekend Esri joined forces with Airbnb, the City of San Francisco’s Mayor’s Office of Innovation, and GAFFTA to put on the Discover SF Hackathon.  This event brought together designers, developers, and entrepreneurs to hack the urban experience by promoting discovery and exploration for residents and tourists alike.

A sneak peak at GeoTriggers

This was also the first public preview of Esri’s new ArcGIS Geotrigger Service!  The API and service connects you with your surroundings so app developers can provide physical context to users.

To help people get up and running, some of lead GeoTrigger developers were on site to hand out special AppIDs, documentation and samples that the teams could use to build a their own trigger-enabled apps.

The Winners

At the end of the event, 12 projects were submitted to the Discover SF Hacker League site.  The projects included some of the most creative and innovative ideas you could imagine.  We even saw an app that coders could use to find other coders based on the location of their commits on GitHub – all in real-time!  How cool is that?

Grand Prize + Most Innovative Use of Esri Technology Award

SFPOPOS – This creative team took the overall win which included co-office space for further development of their ideas, $1000 in AWS (Amazon Web Service) credits, plus the Esri award for $2000 plus $250 in ArcGIS Online credits.  Designers and web developers on the team created a simple but elegant website to explain what POPOS were and to showcase the application.  The developers on the team actually implemented the ArcGIS GeoTriggers API with a real working iOS mobile application.  The application automatically detected when you were within a certain proximity of a POPOS and the trigger would automatically send you a text message notification.  You can learn more about the app here.

The Airbnb Innovation Award

Fraudio Tours – This team took home the Airbnb Award for $500.  Their mobile web application used your geolocation to provide an audio walking tour of the area (like one you would expect in a museum).  The difference was that the information is written and narrated by a comedian.  What an interesting and entertaining way to discover what’s around you!

Esri and Twilio Service Credit Award

Rainbowsomewhere – This team won the Service Credit Award which included $250 of ArcGIS Online credits and $250 Twilio credits.  The web application worked with local weather services to predict the location of rainbows.  The application used your geolocation and an algorithm to calculate the potential locations in 10 minute intervals and displayed the information on a map.

Where did the data come from?

A huge collection of data made available for this event and the majority of teams took advantage of the extensive collection from the City of San Francisco’s DataSF Portal – https://data.sfgov.org/ and the ArcGIS Online organization.  Most of their data was published as either ArcGIS Online maps or feature services, so developers could easily load these into their apps.

 You can find a list of all of the data resources on the Hacker League site.

What about custom datasets and services?

In order to provide a way for participants to publish and access their own data, we also spun up an ArcGIS Online Hackathon Maps and Services organization.  This included a ton of credits that participants could use to upload, host and even analyze their own data.  And it didn’t take long for a number of teams to jump on board and spin up a few of their own custom geo services.  Here is an interesting one that was created to show the location of Art and Cultural Facilities.

In the end…

Overall the hackathon was a huge success.  Not only did it provide an opportunity for the community to demonstrate what apps and tools they thought would be most beneficial to discover the city, but it also gave the City of San Francisco and the participating organizations some great ideas of what they can do to help solve the problem too.  Not to mention, there were some awesome prizes and awards given away!

From all of the Esri folks that were there, we really enjoyed working with all of the teams and organizations involved, and we look forward to seeing you again at the next one!

In this post, I will discuss a practical use of filtering, the ability to limit the display of features on a map, and demonstrate some analysis capabilities now available to ArcGIS Online for organizations. Currently, the ability to perform analysis is a beta release. To be clear, there is no credit cost for performing analysis during this beta period; however, credits are charged for the hosting of the feature services that are the result of an analysis. When the beta program ends, analysis tools will consume credits.

Filtering a layer for specific types of crime

For this example, I have downloaded crime data from the Los Angeles County GIS Data Portal, made available by the Sheriff’s office. I have geocoded the CSV file and shared it as a feature service from ArcMap to ArcGIS Online. This layer, organized by crime type, contains the time and date of each incident as well as addresses at the block level. ArcGIS Online has the ability to filter feature services. Applying filters so only certain crimes, such as arson, display on the map can provide insight into patterns of where arsons are set and help law enforcement officials prevent future attempts. To do so, click the arrow  next to the crime feature layer in the contents window and click Filter.

Filters can also be interactive, allowing law enforcement officers to explore the data. To make the filter interactive, check the box and provide a prompt and hint about the available values in the layer. The prompt can be used to ask for a type or category, while the hint can be used to display the possible filters. Prompts and hints will help guide officers toward other filters they might want to apply on the feature layer. To create filter expression for specific crimes, choose the field as ‘Crime’, the operator as ‘is’, and choose a unique crime type.

NOTE: The filter has to be applied to a value, field, or unique type in order to carry over into the filter web map application.

For example, law enforcement officials might set up an interactive filter so other divisions can focus on different crime types, such as aggravated assault or narcotics. By reading the prompt and hint you have entered, officers will know they can type in other crime hints to filter for specific crime types.

Adding a buffer to show crimes near public schools

In the Performing Analysis beta release there are four types of analysis that can enhance the map’s story: aggregate points, find hot spots, overlay layers, and create buffers. Once certain types of crime have been located, it could be important to find crimes potentially harmful to children walking to or from school. One way to illustrate crime proximity to schools is to create a 2,000-foot buffer.

For this example, I have downloaded a points-of-interest dataset from Los Angeles County’s Location Management System, isolated Los Angeles public schools in the dataset, and shared the feature layer from ArcMap to ArcGIS Online. To create a buffer, click the arrow  next to the public schools feature layer in the contents window and click Perform Analysis, Use Proximity, and Create Buffers.

The resulting buffer can help locate the areas of frequent crime and provide insight to law enforcement officials at what time and frequency are children vulnerable to crime.

Law enforcement officials can also perform additional analysis with these results; for example, they can find hot spots of crime both within and outside of a buffer. These hot-spot results may provide more insight to crime patterns and help abate their occurrence.

Configuring the filter application template for the public

Web maps are very helpful for illustrating buffers and filters, but law enforcement officials could also configure the filter web map application and share it with local residents. To do so, click the Share button in the map viewer. In the display dialog box, click Make a Web Application, find the Filter application template from the gallery, and click Publish. Next, click go to item page and click Configure the App.

The filter application template will use the filter expressions created in the web map and provide a more interactive experience, where the prompts and hints are always displayed so the map’s users are encouraged to engage with the data.

A map tileset is simply a set of images stored on a web server which can be accessed directly. Since tilesets are not referenced as a service, the key to adding them to your web map is to know the URL of the tileset you want to use. Most tilesets will follow the same standard so that the URL ends in /{z}/{x}/{y}/.[image format] where {z} is the zoom level parameter, {x} is the x coordinate parameter and {y} is the y coordinate parameter. Chris used the OpenCycleMap tileset which illustrates the standard URL array:

http://{subDomain}.tile.opencyclemap.org/cycle/{level}/{col}/{row}.png

In order to use a tileset from another provider in ArcGIS Online you first need to discover the URL for the tileset you want to use. Sometimes the provider will publish the URL on their web site, like Stamen. Alternatively, with a little exploratory work you can discover the URL from the web browser. Let’s see how this works with Stamen’s Watercolor tileset.

Step 1. Load the original map viewer. Stamen Watercolor is at maps.stamen.com/watercolor

Step 2. Right-click in the browser window and select Inspect element

Step 3. Select the Resources tab and then navigate to the folder that contains the individual tiles and select a tile file

The individual tile will contain the URL in the standard format. Note it down. This is what you will need in order to use the tileset in ArcGIS Online.

In the example above, you’ll notice that the sub domain is <a>. If you explore some of the other images in the folder you’ll likely find other sub domains used (e.g. b, c, d). These are simply the sub domain location for the particular tile with the complete tileset shared across multiple servers.

So the Stamen Watercolor tileset URL information you require is:

http://{subDomain}.tile.stamen.com/watercolor/{level}/{col}/{row}.jpg

sub domains a, b, c, d

Now you’re ready to add Stamen’s Watercolor tileset into ArcGIS Online using the Add Layer from Web option that Chris outlined in his blog. The Add Layer from Web dialog box should look like this:

Note that the credits information is completed fully. It is important to ensure that you abide by the terms of service of the original tileset provider and give proper attribution. If you are in any doubt as to the attribution required, contact the original tileset provider.

You can either build your own web maps using this method or, alternatively, the Stamen Watercolor, Toner and Terrain tilsets are all available in the Stamen basemaps group on ArcGIS online.

It is also permissible to publish any custom maps from your MapBox account in ArcGIS Online.  The process is the same, except the URL must reference your MapBox account and the Map ID. Let’s say my MapBox  username is <kennethfield> and the Map ID I want to use is <b34d0wdj>, then the URL and sub domain information I require to add a tileset into ArcGIS Online is as follows:

http://{subdomain}.tiles.mapbox.com/v3/kennethfield.map-b34d0wdj/{level}/{col}/{row}.png

sub domains a, b, c, d

Attribution for MapBox tilesets: Data © OpenStreetMap contributors. Design © MapBox.

An example Mapbox tileset used as an ArcGIS Online basemap inside an Application template can be seen here

Adding tile layers from other providers gives you even more flexibility in styling your ArcGIS Online web maps. In addition to Esri basemaps, you can now make use of the free basemaps from Stamen. You may also have authored and published maps using MapBox and TileMill which you can now reference in ArcGIS Online web maps as an alternative basemap. This gives you considerable flexiblity in styling your basemaps and ensuring they suit the purpose of the map theme or story you are illustrating. Get creative!

Jump-start your conference a day early by joining us for the 20^th annual pre-conference Water Resources Workshop on Sunday July 7^th. The Water Resources Workshop is a FREE, full day workshop focused on emerging trends in the integration of GIS and Water Resources and how you can use them in your work.

The web is now an everyday piece of GIS, and much of our focus in the workshop will be about the ongoing evolution of water resources and the web. The web is where you get your data, where you put your data, where you find and interact with tools and models, and where you publish results. We will have presentations from Esri staff, academia, industry partners, and ArcGIS users showcasing what resources are available, what new things are being worked on, how you can use them, and what you can do to contribute.

We realize the workshop falls on the weekend and we’re competing with the urge to be on the beach. So, we promise make the day fast and fun, with plenty of demonstrations and discussion, and by all means keep it relevant to helping you do your job better.

If you work in water resources and this is your first visit to the Esri Users Conference, we strongly encourage you to join us, learn what’s happening, ask questions, and use this as an opportunity to help plan your week. For all you regulars who we see every year, we look forward to seeing you again and sharing some fresh ideas and information.

Yes it’s true! A full day of cutting edge education on emerging trends in GIS and water resources. For many years this was a paid seminar and not only is it now free, but this year also includes a free breakfast provided courtesy of our partners at Kisters.

The meeting agenda and registration information will be available in the coming weeks, so check back to the Hydro Blog and stay tuned to our tweets (@HydroTeam) for these announcements. We look forward to seeing you soon!

Layout Window

Within the Layout Window, map elements are listed in their draw order. Each map element has an icon that represents that element type.  For example, the Layers data frame has the same layers icon found on ArcMap’s Table of Contents window.

The key capability of the Layout window is the ability to search for a map element in a crowded map layout. There are two ways to search in the window – by looking at the icons in the element list or by typing the name of the element in the search text box. Once you find the element you can use the context menu (by right-clicking) to lock, group, change its order, and so forth.

Context menu options when right-clicking on a map element

Grouping:

If you have a cluster of map elements of the same type (let’s say, all your north arrows) you can use the group option to manage them as a single grouped element. Simply select the map elements in the Layout Window that you want to group together by holding down the Ctrl key on your keyboard, right-click and select Group. Then name the group appropriately. To ungroup, you can right-click on the grouped element and choose Ungroup.

Re-ordering:

You can change the map element’s draw order within the element list (which will automatically be applied to the element on the page layout).  You can choose Bring to Front (move element to top of the draw order), Send to Back (move element to the bottom of the draw order), Bring Forward (move element up one position in the draw order), or Send Backward (move element down one position in the draw order). You can change the draw order either using the context menu or by simply dragging and dropping the element within the Layout window.  As you can see in the image below, I dragged the “Grouped” map element from above to below the “Scale Text” element.

Drag and drop an element to change its draw order

Locking:

Locking is particularly useful when you want to prevent certain elements from being modified. Once locked, a red lock symbol appears next to that element. You cannot move, resize or delete that element as long as it’s locked.

Red lock symbol appears next to an element when locked

Draft mode:

Setting an element in Draft Mode allows you to turn the element off – the element remains in position on the page layout but does not render. In the image below, the Layers data frame has been set to draft mode in order to edit other map elements.

Element set in draft mode does not render

As you edit/move/resize other map elements the data frame doesn’t constantly redraw each time, which makes it easy to quickly edit your page layout. We can unset draft mode by right-clicking on the Layers data frame and choosing Draft Mode. If you have multiple data frames use the Activate Data Frame option to activate them one by one.

This concludes a quick overview on using the Layout Window to centrally and efficiently manage your map elements!

Content contributed by Heather Eisan

May 6, 2013 Deployment

March 29, 2013 Deployment

March 22, 2013 Deployment

February 28, 2013 Deployment

February 11, 2013 Deployment

January 16, 2013 Deployment