Hand-drawn Map of My Old Neighborhood



This is a hand-drawn map of my old neighborhood, in Carlisle, PA circa 1984-1992.  It is drawn from memory and shows locations that were important to me, mostly friend’s houses and a few local businesses.  I could tell you 100 funny stories that happened within the confines of this map.


Landscape Interpretation – Ilwaco, WA

Landscape Interpretation


Landscape Interpretation

Ilwaco, WA


Shane Canyon Walsh

Rowan University

Remote Sensing / Air Photo Interpretation
09 November 2016




Location:  Ilwaco, WA, the Columbia River and the Pacific Ocean.  Ilwaco, Washington, is located at 46.02984 decimal degrees north and 123.8889 decimal degrees west.  Ilwaco is a small town on the southwest coast of Washington state, where Washington borders the state of Oregon.  The Columbia River flows west towards the Pacific Ocean acting as a natural border between the two states.  Ilwaco has just under 1000 residents.  The town is roughly 17 miles northwest of Astoria, OR, the nearest major town, and 100 miles northwest of Portland, OR, the nearest major city.

Physical Features:  The physical features present are peninsulas, jetties, spits, islands, forests, beaches, harbors, lakes, rivers and ocean.

Land Covers:  The types of land cover present are open water, low intensity residential, commercial/industrial/transportation, shrubland, forested upland, barren and transitional.

Elevation:  The elevation ranges from 0 meters at sea level to 125 meters, at its highest point in the scene.

Source:  I downloaded the Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data dated 19 Aug 2016, at http://earthexplorer.usgs.gov.  I downloaded the ASTER Digital Elevation Map (DEM) data dated 17 Oct 2011 (two files in order to Mosaic), at http://earthexplorer.usgs.gov.

Normalized Difference Vegetation Index (NDVI):  NDVI is a way of determining how green or lush a particular area is.  The images are taken from a satellite and the range of NDVI is calculated by the ratio of how much visible (VIS) and near-infrared light (NIR) is absorbed and reflected in the scene.  The equation to calculate NDVI is (NIR-VIS) / (NIR+VIS).  The range of NDVI for the scene is -.28 to .90.  The thresholds used were -.28 to .27, which indicated water, .27 to .70, which indicated no vegetation, .70 to .80, which indicated low vegetation and .80 to .90 for the high vegetation.  In the scene 171.63 square kilometers were water, 15.39 square kilometers had no vegetation, 17.63 square kilometers had low vegetation and 20.35 square kilometers had high vegetation.  76.28% of the scene was water, 6.84% had no vegetation, 7.84% had low vegetation and 9.04% had high vegetation.

Water:  To determine where water is in a given scene, two approaches are used.  The first is the Kauth-Thomas Tasseled-Cap Index, which uses combinations of readings to determine the wetness of the soil.  The range of Kauth-Thomas wetness in the scene is described as -.28 to .42.  The second approach is to construct a runoff model to see where the minor and major water pathways are.  The patterns of these minor and major pathways do not follow any general direction in the cape/peninsula, due in part to the elevation and the slimness of the land.  A drop of water will run off to the west or east depending on which side of the hill it lands on.  In the other areas not on the cape, the water appears to come down from the higher elevations toward the river or ocean.

Summary:  This type of study is useful as a way to determine sets of information about an area in particular.  This study was a first step to answering more questions.  Taken further, slope and aspect could be determined.  This would aid in deciding where to put an observation tower, for instance, over-looking the Pacific Ocean, while still being able to see the Columbia River, as well as having the least physical walk up.




Figure 1 – True color composite of Ilwaco, WA, the Columbia River and the Pacific Ocean


Figure 2 – ASTER Digital Elevation Map of Ilwaco, WA, The Columbia River and the Pacific Ocean


Figure 3 – Continuous NDVI for Ilwaco, WA, the Columbia River, and the Pacific Ocean


Figure 4 – Discrete NDVI for Ilwaco, WA, the Columbia River, and the Pacific Ocean


Figure 5 – NDVI values, including minimum and maximum values


Figure 6 – Kauth-Thomas wetness image with major and minor streams

Qualitative Research and Mapping

Related to current internship with Rowan University Community Visualization/Planning Lab


Feelings Associated with Locations in

Kensington, Philadelphia

Shane Canyon Walsh


13 December 2016


North Kensington, a section of the Kensington neighborhood in Philadelphia is one of the roughest neighborhoods in the city, if not the entire country. It is an area with high above average unemployment, drug use, prostitution and violent crime. The slow, inevitable march of gentrification has finally appeared at the front door of North Kensington, the Lehigh Avenue Viaduct. The viaduct acts as an informal border between the up and coming neighborhood to the southwest and the downtrodden neighborhood to the northeast. It is an imposing presence. At some points the viaduct is a high wall that stands 2 – 3 stories tall, while at other points it is a tangle of barbed wire and broken down cars.

The reason I chose this study was two-fold. The first being that my family and I lived at the corner of Tulip and Boston, just a few blocks south of the viaduct, for three years. I saw effect the viaduct had on people and the neighborhood. We would not go for walks that way and we would not go for bike rides that way. I drove through that neighborhood quite a lot as it was a quicker way to get up to the Aramingo Shopping District. It was depressing to see the broken down houses, piled up trash and monuments to dead loved ones. I also played in a band and the place where we practiced at was on Clementine Street near Frankford Avenue. The drive from Lehigh Avenue to Frankford to Clementine was terrifying. The second reason is that I am more of a visual, creative, artistic person, as opposed to a number crunching person. I spent a decade as a photographer and have been writing music since my teenage years, so the combination of mapping emotions, feelings and photographs in my old neighborhood seemed like a perfect fit.

A handful of studies have been conducted about how to revitalize the viaduct and have it no longer be an imposing, negative presence but to have it be a gateway to a redeveloped North Kensington. Most of these studies show the viaduct as a modern green space akin to the High Line in Manhattan. I once visited a friend who works for Olin Architecture in Philadelphia and they have plans for brownfields sites all over the city. One large print out on the wall was their re-imagining of the Lehigh Viaduct. The plans were along the same lines as others I have seen.

As part of this effort to help re-imagine North Kensington, a project was started that would include residents of the neighborhood in the process. This research, conducted by Mahbubar Meenar, PhD, GISP, and his research team, would consist of having residents take photographs of locations in the neighborhood and attach short descriptions which included their feelings or emotions about that space. In his words “the data were collected and compiled by my research team working on a project funded by the US EPA – titled Brownfields area-wide planning in North Kensington, Philadelphia. All interviews were conducted in summer 2016.” A description of the project from his website reads “the one hundred eighty-one-acre project area includes brownfield sites adjacent to the Lehigh Viaduct, a freight rail corridor located along Lehigh Avenue, between Kensington Avenue and Interstate I-95, with the main focus between Kensington Avenue and Tulip Street. This area served as a key industrial manufacturing center and transportation hub for anthracite coal from northwest Pennsylvania in the nineteenth and early twentieth centuries, but experienced rapid decline when these industries left in the 1950’s and 1960’s. This left the area with problems similar to other post-industrial neighborhoods: blight, crime, general disinvestment, extensive trash from illegal dumping, and vacancy. A quarter of all parcels in the project area are vacant, representing over forty percent of total land area.”

Data and Methodology

Dr. Meenar and his research team collected roughly one hundred and sixty locations, most with corresponding interviews. These interviews ran the gamut of emotions that run through the neighborhood, from disgusted to ecstatic. Some contained nothing more than a few words; feelings, like frustrated, hopeful, or angry, while others recorded short, personal anecdotes, but no emotions or feelings. Twenty-five or so did not have any text at all. I wonder if these should be deleted from the project or if they still have value. Most had photos attached to their interviews to leave a visual reminder of what they saw and more importantly, how they saw it. This information was compiled into a spreadsheet.

I created a new spreadsheet with roughly forty locations (Fig. 8), adding a field which contained the feelings or emotions attached to the site. It was then that I noticed a need for some revisions in the spread sheet. There was at least one redundancy I noticed, where one researcher recorded a location as “Somerset and Tulip” (loc. 3 in original spreadsheet) and another location was noted as “Tulip near Somerset” (loc. 99 in original spreadsheet). It might be more efficient to combine these, although they may not be exactly the same location. I do not know if there were more of these types of redundancies. I also noticed that one location “Somerset between Ruth and Huntingdon” (loc. 11 in original spreadsheet). With my knowledge of the neighborhood I know that Somerset does not intersect Huntingdon. Somerset is five blocks north of Huntingdon and runs parallel with it. I assumed that the researcher meant “Somerset between Ruth and Kensington” and this was merely a typo, which would make more sense. I do not know if I was allowed to make a change, although I did. I noted in my map note for that location that the original entry stated “between Ruth and Huntingdon”. I do not know how many of these errors occur in the data.  Once I accounted for these typos, redundancies and entries with stories but without feelings attached, or entries with no text at all, I was left with twenty-five locations, which was the minimum necessary for my research.

I conferred with some of my colleagues in class who were working with ArcGIS Online.  I decided to try this approach. The interface and speed of changes appealed to me for this particular project. I created two or three of the five maps before I ran into a major problem, which I will get into later.  The qualitative nature of this data is interesting in that it records what people feel about a location, not necessarily right or wrong, but how they feel, which when viewed a certain way, cannot be right or wrong. Some locations had more than one feeling, most had three or four, and not all of those three or four were positive or negative. Most were mixed like “confused, delighted” or something similar. I struggled with how to display these feelings in a beautiful, aesthetically appealing way. I thought I would play with the idea of colors because of seeing the example of color wheels with emotions. Eventually I decided I would attach shades of red to locations with negative feelings and shades of blue to locations with positive feelings. The shade or saturation of each color would be determined by how visceral the feelings were, i.e. the darker the color, the more forceful the feeling. I also wanted to divide the positive feelings into one set of shapes and the negative feelings into another set of shapes, to differentiate them more.

Entering the locations was an iterative process my workflow my picked up as I  became comfortable with the application. Once, I had them all entered, I went through the images attached to the interviews and chose what I believed to be the most appropriate image for that location. I then uploaded these to ArcGIS Online and slowly, painstakingly added them individually to each map note/location. One criticism I found with ArcGIS Online is that you cannot edit or manipulate multiple map notes or images at once. For example, you have to make each image public, one by one. If you are able to edit multiple files at once, I could not find a way.

The main issue that arose for me was that I could not get excited about seeing nine blue circles and sixteen red triangles. I was worried that I would have five uninteresting maps at the end of this project. Then the most amazing aspect of ArcGIS Online became apparent: all of the wonderful data sets that others have created and uploaded into the open source area of the website.  This allowed me to back up the positive and negative aspects of the feelings with hard data that would possibly highlight why this neighborhood is in the condition it is. In the back of my mind I imagined these maps being used as a link between the residents of the neighborhood and the developers, showing each other what mattered on the same piece of paper or computer screen.

The first map (Fig. 3) is fairly basic. It shows a base map of Philadelphia with a layer highlighting the neighborhoods included in the study. I may have included a few more neighborhoods than technically in the study, however they were all part of the old, original extent of Kensington. Today these neighborhoods make up the are known as the River Wards. The neighborhood data was provided by Azavea, John Branigan, Megan Heckert, Robert Cheetham, and Daniel McGlone.

All of the feelings, both positive and negative associated with their locations was the topic of my second map (Fig. 4). I also included the location of my former residence, to show how close I was these areas. These data sets were combined with a layer showing incidents of crime for the current year, 2016. I changed the colors of the incident layer to blue and red to connect with the colors of the positive and negative locations. I created a word cloud with all of the feelings associated with all of the locations, both positive and negative. The crime Incident data was provided by the City of Philadelphia (maps.phl.data).

For the third map (Fig. 5) I focused on the negative feelings. Along with these feelings I included some negative influences that have an undeniable effect on the residents of North Kensington. I displayed only the red triangles along with two other layers. One showed the unemployment rates, the other showing the vacant land. I also included a small overview map showing the unemployment rates for the entire city. I did this to highlight that fact that the areas with the highest unemployment rates tended to be the areas with the highest crime, drugs, and violence. I included a word cloud with all of the negative feelings and I changed the symbology of the unemployment rate layer to red to connect to the red triangles. The unemployment data was provided by Bonnie521, the vacant land data was provided by lmedsker_PHSonline.

At this point I encountered my major problem. In ArcGIS Online you cannot export them as PDF’s to print. This was an issue for me as I had to put my five maps into PowerPoint. I eventually found a video on YouTube by researchers at a joint project between the University of Tennessee, the Tennessee 4-H GIS Program and the Ethiopian Agricultural Transformation Agency. The researchers were using ArcGIS Online in Ethiopia due to its speed and ease of use, however they ran into the same problem when they needed to give a presentation. Their work-around was that they basically cut, copied, pasted and mosaic-ed bits and pieces of the map from ArcGIS Online into PowerPoint. Realizing that I had gone too far down the rabbit hole, so to say, it was too late for me to turn back. I appropriated their approach as my own and pushed forward. In the maps that you see cut and pasted everything, apart from the base map and the locations/feelings. The legends, scale bars, word clouds, titles, subtitles, credits and north arrows were all put in separately. The legends took the most time as some of them had twenty or more components. Once I fell into a groove and figured out the process I became comfortable with it.

For my fourth map (Fig. 6) I connected the positive locations to healthy options to show that not everything in North Kensington is negative. I chose walking, biking and access to healthier foods because of their relationship as factors to healthy living.  The symbology throughout this map is cooler colors as they are more relaxing to look at, in contrast to the map before. Once again, I included a word cloud with just the positive feelings associated to the locations. The healthy corner store, and healthy Chinese takeout data was provided by the City of Philadelphia (maps.phl.data), the farmers’ market data was provided by abrown_citygov, the bike lane data was provided by jessica.hammond.phl.

In the final map (Fig. 7) I wanted to show the all of the positive and negative locations combined with neighborhood resources or assets. These are locations or organizations that the residents can be proud of (i.e. pools, spraygrounds, playgrounds), in addition to knowing these organizations can help them when they need it (i.e. summer meal sites, hospitals, WIC offices, community programs). I was happy to see that the New Kensington Community Development Corporation (NKCDC) was building a new set of offices in the Orinoka Mills Building. I know not many organizations have done more for Kensington and Fishtown than the NKCDC. Even with Sandy Salzman retiring recently I am sure the NKCDC will be a guiding resource in the neighborhood. The base map layer is one the shows the future projected population growth, which highlights the potential for the redevelopment of the vacant areas in the neighborhood. The population growth data was provided by the studley_nif, the summer meal sites data was provided by amory.hillengas.phl, the community programs data was provided selyukin, the pools/spraygrounds was data provided by johnson, the playgrounds, hospitals and WIC offices data provided by City of Philadelphia (maps.phl.data).

I combined all four of my main maps into one for the web application, bringing a little bit of each map into the one online resource. I feel like the printed versions of the maps really lost a lot of the context in that you could not access the pop ups or see the photos. I also put links to articles in some of the pop ups about that location.

Results and Conclusion

Using twenty-five locations felt like too little but I had to complete other projects besides this one. I would like to see what becomes of the map with all one hundred and sixty locations. With all of the locations mapped maybe a pattern or better understanding will be realized. I felt I needed to bring other data into the project to make it not look uncomplete or amateurish. This may detract from the overall goal of the project but it helped me show possible reasons as to why the neighborhood is in the state it is in.

One issue I cannot shake is how many of the interviews recorded multiple feelings, both positive and negative. This made me wonder about the human element of this qualitative study. One person could have been having a good day, overall, and one person could be having a bad day, overall. Both of these people could see the same location in two different ways, positive or negative. Another thought was that a member of the research team could have had a bad personal experience at a location and that would obviously affect their feelings toward that place. This is why I think it might be necessary in the future to combine these feelings with quantitative data, or maybe have members of the research team not be from Kensington and see if they record different feelings and emotions.

The uniqueness of this study is that the data is qualitative, not quantitative, in nature. This is interesting in that developers, planners or architects may be more concerned with quantitative data, lengths, heights, dollars, etc. While the residents in the neighborhood just want to feel safe at night and feel proud about their neighborhood. As I wrote before, I envisioned these maps as part of bridge to connect the members of the various development entities with the residents of the neighborhood that needs revitalized. The developer’s plans have to include the wishes and thoughts of the people living in that area. They should not barge in and just open up Starbucks. The last thing Kensington needs is a Starbucks. The neighborhood has to dig out of the hole that it is in, in a combined effort from everyone involved.

Figure 1 – Feelings Attached to Locations in North Kensington, Philadelphia Story Map:

Screen Shot 2016-12-13 at 10.32.48 PM

Figure 2 – Feelings Attached to Locations in North Kensington, Philadelphia Web App:

Screen Shot 2016-12-13 at 10.33.35 PM

Figure 3 – Map Showing Overview of Philadelphia Region Showing Research Area:

Screen Shot 2016-12-13 at 10.46.17 PM

Figure 4 – Map Showing Feelings Associated with Locations in North Kensington,

Philadelphia – Crime Incidents:

Screen Shot 2016-12-13 at 10.46.38 PM

Figure 5 – Negative Feelings Associated with Locations in North Kensington,

Philadelphia – Unemployment Rates and Vacant Land:

Screen Shot 2016-12-13 at 10.46.54 PM

Figure 6 – Positive Feelings Associated with Locations in North Kensington,

Philadelphia – Healthy Options:

Screen Shot 2016-12-13 at 10.47.08 PM

Figure 7 – Feelings Associated with Locations in North Kensington, Philadelphia –

Neighborhood Resources:

Screen Shot 2016-12-13 at 10.47.24 PM

Figure 8 – Spreadsheet with Chosen Locations, Location Type and Feelings:

Screen Shot 2016-12-13 at 11.04.46 PM