ArcGIS Online Map Viewer Analysis Tools Review 

Tool Name: Drive-Time Buffer

Description: creates polygons around a point indicating how far of a drive or walk it would take to reach it

 Documentation on Tool

Entry by: Nate Vincent 

Findings/summary/reflection: 

Time management is an integral part of commuting, especially in one of the largest campuses in America. At Penn State, thousands of students take a class in The Forum during their early years for required classes such as STAT 200 or MATH 110. However, it can be confusing for a first-year student to navigate their way across campus and estimate the time it would take in this new environment. In GIS, Drive-Time Buffers allow the user to set proximities around a point to show how long it would be to travel to a point. Proximity buffers such as Drive-Time are available on ArcGIS Online. Note: I had to use the Map Viewer Classic to set it up because Map Viewer did not let me create a compatible point.  

I first searched up the address of the Forum in the search bar found at the top right, it does not take you directly on top of the building, but you are able to drag the point to it. I clicked the underlined “Add to Map Notes” found within the pop-up menu and renamed it to “The Forum” and selected Change Symbol to turn it into an academic icon. Then, you click Analysis (found on the ribbon), click Feature Analysis, then under Use Proximity, click Create Drive-Time Areas. Make sure your selected point is the renamed Map Notes point layer. Since most students who travel to classes walk, you should click the Driving Time drop down and select Walking Time. You are free to skip to Step 4 and rename the layer, unless you would like to set a Barrier Layer for another analysis. After you select Run Analysis, you can change the Symbology by clicking Change Style, or the shapes icon under the layer in the Contents Pane. Color and Amounts (Color) and the theme High to Low is the most appropriate since we are trying to show the different ranges of time it takes between each value created in the Analysis. This is where customization can come into play, and you as the user can decide which colors appear best in the Symbols option. I chose a symbol that starts with tan being the closest time to walk to The Forum, then progressively becomes a darker red as you are moving away because those areas take much longer to get there. You are also able to change the ranges of the walking time by selecting Classify Data. Of course, there are more capabilities than this when it comes to buffers, but this is a good first step to learn how buffers work in ArcGIS. 

In my first attempt to create a proximity buffer, I found it hard to create a point on the map that can be used for the analysis. However, after searching up a YouTube video, I was able to create one through the Map Notes option. At that point, it was just deciding the ranges of time for students on campus, which could be refined with classifying the data. While I found it interesting that I could create these time buffers with driving and walking, there should be an option for biking and scooters as well, as there are hundreds used on campus. Because each person has different levels of speed, depending on their ride and their ability, ArcGIS should have an option for average speed. We can figure out this average speed through surveys sent out by email, and then we can figure out the average time it takes for bikes and scooters as well.  

ArcGIS Online Map Viewer Analysis Tools Review 

Tool Name: Find Centroids 

Description: Finds the center of each feature 

Link Documentation on Tool

Entry by: Nate Vincent 

Findings/summary/reflection: 

A centroid is the center of a polygon, line, or multipoint feature. While it has no official use, users can add centroids to their maps for their desired purpose. I decided to create centroids for every county in PA, just to see where the center of each were. To do this, I went on   https://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=24 and downloaded the shapefile. I inserted it into my map, clicked Analysis and searched for Find Centroids I used my PaCounty202303 layer as the input layer, then renamed the result layer to Centroid of Each PA County. Here is the resulting map: 

I found it interesting where the center of each county was because in most of the counties, the central point is not contained within a city. In the case of Centre County, the centroid is over Bald Eagle mountains, and the closest town is Bellefonte. 

Nathan Vincent is a Sophomore from the Lehigh Valley majoring in Geography in the college of Earth and Mineral Sciences. Nathan’s interests include Land Use, Transportation, GIS, and Information Technology. Nathan has been working at the Donald H. Hamer Center for Maps and Geospatial Information since April 2023.

Analysis in ArcGIS Online: A review of analysis capabilities 

This blog post is a summary of the materials discussed in the Esri Training module “Analysis in ArcGIS Online”, and my findings while completing several tutorials. The presenters are Derek Nelson, John Thieling and Allison Rost. These presenters explain the different types of analysis and introduce ArcGIS Online and the new Map Viewer. The goals of this training module are to: 

• Select the most suitable analysis tools based on your dataset and the questions you aim to address 
• Enhance your analysis project by leveraging the resources available in the ArcGIS Living Atlas 
• Conduct analyses on feature layers to identify specific locations, uncover patterns, and carry out proximity-based assessments 

Here are some key notes I took during the presentation:  

Why perform analysis in ArcGIS Online? 

• Cloud-based 
• Access to public data- layers and services 
• Share results and maps, making it easy to integrate with apps 

Types of Analysis:

Feature analysis – 

• Uses Vector data (Points, lines, polygons) to represent geographic features 
• Summarizes features based on geographic location, measure distances around or between features, and quantifies spatial patterns 

Raster analysis – 

• Analyze and process imagery and raster datasets – You are basically analyzing pixels 
• Analyze imagery to derive surfaces via:  

• • Algorithms 
  • Spatial Patterns 
  • Terrain 

Learn more about the different types of spatial data

Credits –  

• A currency used in ArcGIS that are used when performing analysis 
• Can be capped by the administrator. As a Penn State user, if you need additional credits, please reach out to a Penn State ArcGIS Online Administrator. 

The Map Viewer 

When we open the Map Viewer:

Everything to the left is the mapcentric capabilities –  

• Layers 
• Tables 
• Basemap 
• Charts 
• Legend 
• Bookmarks
  • You can use this tab to save specific areas you are observing 

This section (37:50) explains the step-by-step process when performing spatial analysis on ArcGIS Online. This is the completed workflow for analysis in ArcGIS online: 

• Ask questions about the data you are using and what tool can be used to analyze your data 
• Prepare the data by loading it into ArcGIS Online as a web service, or enriching it with other data to make it more suitable for what you are studying 
• Analyze the data, but keep in mind you may have to use different analysis tools like join, summarize nearby, or even create buffers 
• Review the results to determine if you need to modify your analysis, or include a element that was left out 
• Publish the information, a great example of this is to make a Story Map with your narrative of the analysis 
• This allows stakeholders to make decisions, which repeats the process 

Analysis Capabilities 

This next chapter describes the tools and functions, authoritative content, Analysis history, and charting. The presenter then adds a layer of all the US Counties to demonstrate some of the functions found to the right of our map 

The filter tool is almost like a query, where you can write an expression that will select an area

The presenter then goes into analysis tools and uses the enrich layer tool to add sports fan data into his selected state, Texas. In my example, I filtered the county data to Pennsylvania, and added crime data from the enrich layer tool. In the ‘add fields’ section of the styles tab, I was able to choose different types of crime for my map, then set the style to ‘chart and size’ to display all types of crime across each county. On the ‘Configure charts’ tab, I chose the same variables which allowed me to display a statewide crime index. 

Testing an analysis tool: Join Feature 

In this part of the video, the presenter filters the United States Counties layer into only those that encompass Loma Linda, California, and enriches the layer with health data such as average alcohol consumption, etc.  Using a table that displays the quality of life in this area from an outside source, the presenter explains the Join Feature , an analysis tool that transfers the features or attributes from one source into another, based on spatial or temporal attributes. In this case, the table  joined to the enriched layer is spatially based in Loma Linda and was able to connect both together using both columns that displayed the County FIPS (Federal Information Processing Standard, an identifier for counties and tracts).  

When I repeated these steps, I decided to use the tracts of the counties within Loma Linda (San Bernardino and Riverside) and enrich them with the demographic data of the people who live in these areas. Since the presenter did not provide the exact table necessary to replicate his analysis, I found a dataset online from the Census of the walkability index of all the block groups in the United States. I decided it would be interesting to see the census data of these defined tracts.  

To do this, I had to find a unifying locator column from both inputs, the Census tract boundaries, and the walkability index. For this, I decided to use the Tract FIPS to join both layers together; However, I had run into the problem of having too large data for the computers to process on Excel, so I chose to only select some of the tracts for the join feature. The result is some of the tracts being shown with the join feature from both counties, and I realize I should have done a much smaller example.  

Nathan Vincent is a Sophomore from the Lehigh Valley majoring in Geography in the College of Earth and Mineral Sciences, with minors in Geographic Information Science, Information Sciences and Technology, and Sustainability Leadership. Nate’s interests include Land Use, Transportation, GIS, and Information Technology. Nate has been working at the Donald H. Hamer Center for Maps and Geospatial Information since April 2023.

Field work using spatial data can be a very overwhelming process to start figuring out without guidance from people with experience. As an undergraduate student, I have been very fortunate to be part of forest biogeography and forestry field work in multiple places studying several aspects of forests. In these experiences I have helped create multiple kinds of spatial data using different methods of data collection and equipment. In this blog post I will talk more about the field work experiences that I have had and what it has been like to go from the field to creating data to making spatial data to be studied. I will also share the resources that the Donald W. Hamer Center for Maps and Geospatial Information has, and how they can enable library patrons to do field work of their own and create spatial data. 

My first field work experience was working with Dr. Alan Taylor, a professor in the geography department, in which we studied forest fire effects in Lassen Volcanic National Park in Northern California. The research we were conducting was a continuation of fire effects studies that the professor has been sampling annually for decades in this area. The professor joined us for the first 10 days of the field work and then the rest of the sampling was done by one graduate student and the undergraduates’ students including myself. We conducted this research for 7 weeks over the summer of 2022 and sampled 3 kinds of plots both inside the park and in a nearby experimental forest. This field work required us to camp for the entire 7 weeks and to do two 3-to-4-day backpacking trips into the back country of the park in remote areas. This experience was extremely important to my college experience and has helped shape my path as a geographer and environmental researcher.  

Dr. Taylor (in the red) teaching us how to preform plot surveys.

The methods of sampling that we did for the research were random plot sampling. We did this sampling using environmental conditions for the two plots we placed within the park boundary. The other plots we sampled are two one-hectare areas within the experimental forest that were subdivided into grids. In these plots each tree was identified and sampled for several characteristics. To find the plots within the national park, we used handheld GPS units that directed us to the latitude and longitude of the plot centers for the plots within the national park. The accuracy of the GPS units could only get us within 10 feet of the plot center, so we used photography and plot data taken in previous years to align ourselves with the exact plot center. For the experimental forest plots, we only needed to use the handheld GPS to locate the corner of the plots. Once we found the corner of the plots, we used the wooden stakes placed there in the past to help mark out the spacing of the grid of the plot to ensure that we repeated the sampling the same way that it had been done in the past. 

A view of Mount Lassen from a charred forest during work.

To make this data spatial and able to be used in a GIS environment we took the data collected in the field back to the lab here and Penn State and entered the locations of each plot and the associated data on the forest that was collected with them. For the randomly placed plots we only had to plug in the coordinates of the plot centers and then include the observations like number and size of trees, burn severity, percentage of shrub, grass and forb cover in addition to some other data. 

My second fieldwork experience was with a new lab in the forestry department under the supervision of Dr. Tong Qiu. The work I conducted for this project was to help build and place seed traps to study how environmental conditions affected the reproduction of trees and how many seeds they produce and how they disperse those seeds. This research was conducted on Penn State property in the Stone Valley Forest area near Shavers Creek Environmental Center. During this same summer, I studied to earn my FAA part 107 drone license to begin learning how to use drones to create local spatial data. Overall, I spent 2-3 weeks creating the seed traps and surveying the points where I placed them in the stone valley forest. Then I spent the rest of the summer studying for the drone exam and spending one day per week doing a resurvey of multiple characteristics of each tree within the plots. The characteristics included diameter at breast height (DBH), canopy status, number of seeds on each tree etc. 

Installing seed traps with fellow student coworker Evan Hackett from the Forestry Department.

To create the seed trap study areas, we used 4 1-acre plots that had already been tagged and had each species of tree identified. These plots also had spatial data that had all the information about each tree and their locations. In these 4 plots I built and randomly distributed 10 traps that capture the seeds of the various trees within the plots. After all the seed traps were placed, I returned to each of them with a survey grade GPS that I borrowed from the Donald W. Hamer Center for Maps and Geospatial Information. Using this GPS and the ESRI app Field Maps I created high accuracy spatial data for the exact location of each of the traps. Using the exact location of the trees and seed traps, we can study the phenology of these trees in the future. This project is still ongoing and will require multiple years to capture the seeds multiple times to then collect and study them.

I also made use of the ESRI field maps app when resurveying the trees from the 4 plots that we were using to ensure I had the right trees. Using the approximate location, the species and associated information, I ensured that I was sampling the same tree with new information showing the growth and development of each tree. I did not finish my sample of these trees because I was working alone and there are 1,400 to be sampled. This project is ongoing, but my involvement has diminished because I am busy with classes and new projects.

Bad Elf Flex Survey GPS used to create spatial data in Field Maps app.

Finally, I am working on a new project that involves field work for my final semester of college. This work is with Dr. Patrick Drohan, a soil science professor in the college of Ecosystem Science and Management. In this project I will go into the field to create spatial data for agricultural nutrient runoff modeling. I will be creating this spatial data by flying a drone with high resolution cameras and multispectral cameras. From the images created with the drone I will use a computer software called Agisoft Metashape to assemble the images into one GIS imagery data layer. Then I will create a terrain model using this data layer by utilizing a technique called photogrammetry. After I create the data other researchers will take over the modeling of nutrient runoff. The end goal of the project is to understand how effective riparian buffers are at mitigating agricultural nutrient runoff. 

Fin learning to fly a new drone at the PSU student farm.

These are some of the ways I contributed to the creation of spatial data from research conducted in the field. This is the kind of work I enjoy most because I can work outdoors while still utilizing technical and scientific knowledge. Some things I want to highlight again for anyone who might want to create their own spatial data are: 

1. ESRI ArcGIS Field Maps. This app integrates very well with other ESRI software’s and makes field-based data collection very easy. Everyone at Penn State has access to these softwares.

1. The mapping resources available to be loaned from the Donald W. Hamer Center for Maps and Geospatial Information. The Bad elf Flex GPS unit was essential for me to create high accuracy spatial data and it integrates very easily with the Field Maps app. 

1. Field based research is hard. It takes lots of time to plan and prepare to ensure you are using your time effectively. Other experiences researchers are the best resources for figuring out how to get started on your own spatial data collection. 

To read more about the GPS resources that the Donald W. Hamer Center for Maps and Geospatial Information has available to borrow read here. 

To learn more in detail about the process of creating spatial data using the Field Maps app see this blog post.

Fin is a fourth-year student at Penn State majoring in Geography and minoring in Soil Science. Fin has had multiple lab research positions where he has contributed to creating spatial data for forest biogeography and forestry research. Fin’s other interests include using drones to collect geospatial data, competing with the PSU soil Judging team and spending time outdoors. Fin has been working at the Donald W. Hamer Center for Maps and Geospatial Information since February 2022.

Introduction

The PA GIS Conference recently took place from April 10th to April 12th at the Penn Stater Hotel and Conference Center, and I was lucky enough to attend! In all my roles as a geography student, a soon-to-be graduate in the GIS field, and a Maps and Geospatial Assistant at the Donald W. Hamer Center for Maps and Geospatial Information, I knew that this conference was a great opportunity to expand my knowledge in the field of GIS.  

The PA GIS Conference is run by Keystone GIS, a non-profit GIS professional association that provides leadership and guidance in the geospatial industry throughout the state of Pennsylvania. This event brings GIS professionals together from every different sector: private, non-profit, academia, government, consulting; the list goes on. The conference featured several keynote speakers from nationwide GIS leaders as well as a variety of presentations from GIS experts across the state.  This post highlights my experience at the conference from the unique perspective as a student in my very early days of what is hopefully a long career with GIS.

Keynote Speakers 

The first of the four keynote speakers was Tim Haynes, Geographic Information Officer and Chief Data Officer for the City of Philadelphia. Haynes’ presentation focused on the power that GIS professionals have in the overall realm of data science, emphasizing all the different skills that someone in the field of GIS possesses. Haynes provided a breakdown of the city of Philadelphia’s GIS network and the different problems and projects they have been tackling; being from the Philadelphia area, this was an incredibly interesting and informative topic to get to hear more about.  

The second keynote speaker at the conference was Keith Searles, GISP. Searles is the CEO and founder of Urban GIS, a GIS consulting firm based in Chicago, as well as founder of EDGE: Ethnically Diverse Geospatial Engagement. Searles’ presentation was extremely informative and engaging; one of the main emphases of the talk focused on enhancing diversity, equity, and inclusion within the GIS field by promoting the geospatial industry to diverse communities. More on the EDGE program can be found here. 

The second day of the conference featured Dr. Joseph J. Kerski, PhD GISP. As an Education Manager and Geographer at Esri, Kerski’s presentation focused on the several forces, trends, and skills critical to GIS in recent and future years. It was extremely interesting to hear how the world of GIS is changing and where the job market is trending. Hearing Kerski’s excitement for the industry, as well as his history with geography, reminded me of why I gravitated towards this field in the first place and why I am excited to continue my journey in GIS. A link to Kerski’s presentation can be found here.  

Closing out the conference during its final hour were Thomas McKeon and Winnie Okello, P.E. with the Pennsylvania Department of Environmental Protection. This presentation was one of my most anticipated, as Mckeon and Okello unveiled and previewed PennEnviroScreen, Pennsylvania’s own Environmental Justice screening tool. As a geography student, I have had the opportunity to study the history of environmental justice and the current screening tools we have available to map it: it is a huge step in the right direction for Pennsylvania to have its own EJ screening tool that can hopefully aid in education and policy measures to reduce environmental racism and promote just environmental programs.

Breakout Sessions 

The first batch of breakout sessions I attended revolved around community service, citizens science, and public engagement. Working at the map’s library, I spend a lot of time looking at historic maps, so one of my favorite presentations was by Mark Connelly, whose talk was titled Understanding Civic Boundary Changes: An Introduction to the Local Geohistory Project. Mark Connelly is the creator behind the Local Geohistory Project, a website that collects and digitizes the history of civic boundary changes across the state. Mark discussed the methods of how legal boundaries have undergone changes in the past, as well as the intensive process of locating the records of these changes across local, state, and federal repositories.  

Another round of breakout sessions I attended was focused on GIS applications in Public Safety and 911. This was a topic that I was heading into completely blind; while I knew that GIS is used heavily in the realm of public safety, being able to see first-hand the advancements that GIS has made in this category was extremely enlightening. Included in this session was a presentation from Melissa Leibert on how GIS Data is being used in the Next Generation 911 call flow. This presentation covered how GIS systems are being integrated into the state-wide transition to from the analog 911 system to the NextGen 911 System. Liebert emphasized how important GIS is in playing the role of routing a mobile GIS call to the correct Public Safety Answering Point to ensure location accuracy. 

Another presentation from this session on 911 and public safety was from Courtney Malott, titled Getting RapidSOS ready with Fugro. This was an extremely interesting presentation on how Fugro, a Geo-data specialist firm, is working on assigning a vertical z-axis to 911 calls that will help first responders pinpoint a location on emergency callers. Using 3D Mapping technology, this system would allow call centers to receive a visual, for example, on where exactly a call might be coming from if the caller is on the 10th floor of a multi-story building. This talk, in coherence with the previous talk on NextGen 911, helped solidify how important GIS has always been, and how much more important it will become, in the realm of public safety. 

Career Panel

For me, the highlight of any of the individual sessions I attended was an early career panel led by several GIS Professionals, including Evo Andreatti, Drew Fioranelli, Bobak Karimi, and Scottie Wall. As a GIS student and upcoming graduate, a panel focused on navigating the field of GIS during your early career days was the most helpful session I could have asked for. Below is a summarized list of some of the talking points and advice that I know I will carry with me while traversing the field of GIS- hopefully these tips will also be helpful to any other students in GIS:  

• Don’t just focus on your technical skills! Employers need great communicators now more than ever; learn common communication skills, and especially how to communicate within the field of GIS 
• When going into an interview, know basic knowledge on the employer beforehand; it should be evident that you are knowledgeable about what the company does and what they stand for 
• Project management skills will get you far! Learn how to emphasize these skills on a resume with buzzwords like time management, organization, collaborative, etc.
• Along the same lines as communication: learn how to write for different purposes. Writing is an extremely important toolset, and different projects will require different writing styles (i.e. technical writing, grant proposal writing, work documentation, emails, abstracts, policy briefs, etc.) 
• It is very unlikely that you will find a job that satisfies 100% of your passion: be sure to find a career that you enjoy working in, but don’t forget to leave time for your passions on the side 
• Finally, don’t be afraid to take risks, especially early on in your career. There are a lot of different career routes one could take with a GIS background, so try and test the waters on what you are interested in before it feels too late. 

Final thoughts 

The PA GIS conference is an impressive conglomeration of people, knowledge, and passion. My goal heading into the conference as a student and upcoming graduate was to expose myself to all of the different applications GIS has in so many different fields. Despite being a relatively niche career field, GIS is an extremely broad-encompassing sector, impacting domains such as environmental protection, public safety, urban planning, historic preservation, and so much more. 

Whether you are a first-year student entering geography or a graduating senior entering the field of GIS, I would encourage any student at Penn State to attend the PA GIS Conference next year. The conference is taking place April 10th- April 12th 2024 once again at the Penn Stater Hotel and Conference Center here at Penn State’s Innovation Park. Not only does this conference expose you to an expansion of knowledge on GIS, but it may introduce you to a career field, organization, or company that you never knew existed and could maybe even end up working in one day.

Mac Caughey is an upcoming graduate at Penn State majoring in Geography and minoring in Environmental and Renewable Resource Economics. He is pursuing certificates in Geographic Information Science, Landscape Ecology, and Global Environmental Systems. Mac’s interests include environmental sustainability, conservation, and food science. Mac has been working at the Donald W. Hamer Center for Maps and Geospatial information since November 2021.