Place and Space

Dot Map of the USA

in , ,

The Weldon Cooper Center at the University of Virginia put together on e of the most impressive maps that I have seen in a while this past June. The Racial Dot Map is:

an American snapshot; it provides an accessible visualization of geographic distribution, population density, and racial diversity of the American people in every neighborhood in the entire country. The map displays 308,745,538 dots, one for each person residing in the United States at the location they were counted during the 2010 Census. Each dot is color-coded by the individual's race and ethnicity.

Racial Dot Map Full Extent

There a couple things that I think are very interesting about the map.  The methods used are pretty cool where the creator, Dustin Cable, coded the map using Python and a whole set of skills that I do not possess.  It highlights the insight that can be gained when skill sets are combined - for instance I am certain that an urban planner would have a deeper understanding at the extent of racial segregation in a city like Boston (please let me know if I am out to lunch here).  This insight is accomplished using a very minimal design (you can toggle names off/on) that is both effective and beautiful. Remember, all of the distinguishable features on this map are a result of dots with each dot representing a person.  Wow.

Racial Dot Map detail of Boston

Finally, the map was inspired by a similar and equally remarkable Population Map of North America created by MIT Medial Lab researcher Brandon Martin-Anderson. The NA Population map contains 454 064 098 points - a very big data set!

Dot Map: Population of North America

The complete description of the Racial Dot Map can be found at the Cooper Center's site. 

Live Maps

in ,

I love the look and feel of live maps that show 'real time' data as it unfolds in the world around us.  Two of my favorites - and older wind map, and a newer births and deaths map - are shown below: Wind speeds are estimates based on data accessed within the hour.

Check out the map - Wind Map

This map's data is accurate (likely) to within a year.

Check out the map - Births and Deaths

The problem with these maps are many - from a cartographic perspective they do not provide a level of detail that makes them useful. For instance, you could not plan on a weather or wind outcome from the wind map simply because the scale is not adequate to pinpoint where you are relative to the wind.

The births and deaths map likely takes an annual number of births and deaths for each country and then scales that number over a year.  If you ran the simulation for a year, you would see an 'accurate' annual rate for each country, that is reflective of the past.  It is not real time data as the creators have no way of knowing, for certain, that the births and deaths are in fact occurring.

The power of these maps, despite their draw backs, is huge.  The wind map, for instance, was mesmerizing as hurricane Sandy made landfall on the Easter Seaboard. I didn't care that the data was an hour old because the visual impact of the map was profound - it gave me a glimpse into the power of the hurricane. As with the wind map, seeing 'real time' births and deaths puts a scale to an annual number that I might read in the paper.  To see all of those births (4.2 per second)  and deaths (1.8 per second) occurring in Africa and SE Asia, and relatively few in N America and Europe provided some insight, still abstract but a little less so, into our global humanity.

yegdt: Land use in downtown Edmonton - preliminary analysis

in , ,

Edmonton's downtown is a growing and changing entity.  In recent memory Edmonton's core has gone from being mostly dead, to having a few sections of vibrant life with interesting restaurants, bars and a wide range of events.  On an infrastructure level, the core is shifting away from surface lots and empty buildings to new developments including high rise buildings such as the Epcor Tower and the Icons on 104th (thanks in part to the muni being shut down). This gives rise to the question of how downtown land is being used.  So I, of course, made a map.

​

The area of this map is defined by the City of Edmonton's Downtown Neighbourhood Boundary. The black polygons on this map represent surface parking lots (~15% of the downtown area). The surface lots, in conjunction with roadways (~12% of downtown area), represent about 27% of the surface area of downtown core.  Buildings occupy 25% and parks space 6%. I was astonished to learn that there are over 2800 trees in the downtown, with over 25 species (tree data from data.edmonton.ca). I captured each one. Rather than document each tree in a huge legend, I created a teaser that allows you to mouse over each point to discover the trees in your neighbourhood.

Some things of note about the map - a full 27% of the downtown core is devoted to cars, and this data does not include parking garages or underground parking. There is more square feet of land dedicated to vehicles than to building footprints, and only 6% to park space.  Wow.

There are two main sources of data for this project. One is from an article about parking in Downtown Edmonton published on Spacing Edmonton.  Kyle Witiw, the author, was kind enough to share his hand-drawn parking data with me.  I then downloaded the Edmonton OpenStreetMap data from an OSM metro extracts site, and teased out the various buildings in the core, compared and corrected the OSM parking data with Kyle's data, and mapped it using QGIS + TileMill and MapBox. Kyle pointed out a couple of problems with my data, including:

The surface parking that used to exist on the NW corner of 104 street and 102 ave no longer exists - the Fox One condo development is currently going up on that site.

I have not yet corrected that.

There are some additional issues with these data. The road data is likely an under estimate as I measured the length of the roads bounded by the neighbourhood boundary, and then multiplied each length by the number of lanes (2) and width per lane (3m) to get the total area. I did not account for parking lanes or turning lanes, as such I think that I have under estimated to total surface area of roadways. Also, several park spaces are not in the data sets - for instance the area west of Canterra Centre at Jasper and 109 is poorly documented and does not include the park that contains the walking/biking trail.  Also with Canterra, Save-On-Foods is missing, as are the condos to the westThe addition of these residential and commercial footprints would slightly increase the overall built-up area in the core.

In addition, the blue buildings have been classified as  a 'mixed use'.  The OpenStreetMap data (the basis for the building and park data) does not define the use of the blue buildings.  I plan on updating the OSM data so that I can, in the future, produce a better map (I will also update the OSM data with Kyle's suggestion above).In summary, the data needs work and the best place to make data improvements is in the OSM database.

Please feel free to let me know what you think.

Toronto Tweets

in , , ,

There is an emerging narrative that characterizes Twitter as a tool of public engagement that can augment more traditional means of consulting with the public (for instance, see here for Environment Canada's commitment to "...implementing more avenues to facilitate online connections with partners, stakeholders and interested members of the Canadian public") .  While there is no doubt that there are many many people who use Twitter (it is reported that there are over 200 million active Twitter accounts)  as a mean of conversing with their elected officials, it is important to remember that Twitter does not include all voices within our Canadian cities.  This blog post is an attempt to understand who might, in fact, be Tweeting within Toronto in a effort to understand who might be Tweeting about Toronto. About this map

A year of geolocated tweets, representing about 5% of the total number of tweets for 2011

[mapbox layers='mattdance.toronto_tweets' api='' options='' lat='43.65969596299056' lon='-79.38002295227051' z='15' width='600' height='400']

Above are Twitter maps of Toronto that represents all of the geolocated tweets for Toronto in 2011, about 1.5% of the total Tweets (in other words, 98.5% of Tweets are not location enabled). The Twitter data were provided by Trendsmap through John Barratt (thank you!). A full sized version of the dynamic map can be found on the Map Box site.

This was a challenging data to work with as it is so large.  I started in QGIS to understand how the data looked and to pair it down to just tweets within the GTA.  I moved the data into Tile Mill by Map Box, and layered Open Street Map data to provide visual context for the tweets. The 'heat map' effect that I used is described here, as I was not able to make the QGIS heat map plugin work, for some reason (please let me know if you can help with this).  I plan on learning how to build a PostGIS database on my computer so that I can do this.

The idea for the map and post came from these three beautiful and interactive twitter maps: (1) London, (2) New York and, (3) Melbourne.

Observations and analysis

A closer look at the map reveals very dense Twitter areas and areas that are very sparsly Tweeted. The most densely Tweeted area is bounded by Bloor Street to the North and Lake Ontario to the South, connected by Young Street.  There is a greater density along the Lake, away from Young to the West. There are also a small number of ghost Tweets on the lake North of Toronto Island.

Area of Tweet density - Bloor to Lake along Young.

These boundaries are visible in the above image.  There are also a couple of identifiable hot spots - the Eaton Center, Rogers Center (cut off in the above picture).  The areas described by this Twitter Density also corresponds with the tourist and suburban destinations - the areas around Young - Bloor - and Front Street, including the sports stadiums, are not just neighbourhood destinations, but destinations for those interested in shopping or taking in the sights in Toronto.

In contrast, those areas that are strictly neighbourhoods, such as Hillsdale Avenue (running east from Young) do not offer that same density.

to_tweets_5_hillsdaleave

The above example shows a middle class neighbourhood within Toronto that does not have a large number of Tweets other than the cluster at the corner of Young Street and Eglington Avenue in the top left of the image.  The Mt Pleasant Cemetery bounds the neighbourhood to the South.

Hillsdale and Forman, Toronto

In addition, poorer neighbourhoods also seem to have a dearth of tweets.  The following image is of the Regent Park area between Dundas and Gerrard.

Regent Park to the East, Young and Dundas Square, with a hot spot of Tweets, to the West.

It is clear from the image that many people are Tweeting on Young Street.  You can even see a hotspot in the Eaton's Center and at the south east corner of Young and Dundas.  Further east, nothing.  From the Regent Park Wikipedia page:

The average income for Regent Park residents is approximately half the average for other Torontonians. A majority of families in Regent Park are classified as low-income, with 68% of the population living below Statistics Canada's Low-Income Cut-Off Rate in one of its census tracts, and 76% in the other (compared to a Toronto-wide average of just over 20%).

Conclusions

I suspect that most of the Tweets that occur in Toronto are from those who live in the region, but who may be downtown for some shopping, to take in a game, or other recreation.  I also suspect that a majority of those who live in Toronto are a small portion of the overall Tweets in the area between the Lake and Bloor, adjacent to Young.

As you move from this area, I suspect that a greater portion of Tweets are made by residents of those neighbourhoods, simply because fewer 'tourists' would travel to these neighbourhoods unless there was an attraction, such as shopping or food.  Although I am only exploring those ~1.5% of tweets that are geolocated, I feel that these are the Twitter users who are most likely to engage with an Open311type of application, to use their smart phones as a means of communicating location details to their municipality.  If this is the case, then those poor areas of the city, potentially the most disenfranchised, will become more so (look at Mark Graham's work mapping  the digital divide in Francophone Africa).

 

Thesis Musings

in , , , ,

The Point

I just completed my MA in Human Geography. My research pertained to people's understanding of location and their communication of that understanding through emerging location based technologies. My entire thesis can be access here (be warned, it is a large PDF via Google Drive).

In an effort at transparency, the next series of blog posts will examine some of the data that I collected pertaining to place and space, and the communication of that understanding through the tools of neogeography. These posts will draw upon two sources of data; mental maps that my research cohort drew, in conjunction with descriptions of the places represented in their mental maps. I will start by framing the discussion with place, space and mapping concepts.

Please note: The University of Alberta’s Arts, Science, and Law Research Ethics Board granted permission for me to post sections of interview transcripts to personal blogs, as did each informant.  Any names that are referenced are pseudonyms.

Place and Space

The notion of 'place' is a common descriptor of the world, and is a central theme in the study of geography (Relph 1976) and other social sciences such as sociology and psychology (Gieryn 2000).  These various disciplines characterize and describe place in a variety of ways (Harrison 1996); for the purpose of this study, place is defined  as being comprised of three dimensions (after Relph, 1976):

  1. Observable activities that occur in relation to the place
  2. The meanings that are created by a person in that location, and;
  3. The physical features that comprise the location's concrete or tangible attributes.

A place is comprised of its physical characteristics, the activities that occur there and the meanings derived thereof  (Devine-Wright 1997). The more familiar a person is with a place, through experience (for instance, through recreational or sporting activities), the greater the meaning that place is likely to have (Lynch 1995).

Maps

Maps are a common metaphor used to describe place  (Zook 2007) where simple points on the map represent a much more complex reality. Lynch (1960) views place as a series of connected locations where individuals mentally organize their spatial environment in predictable ways around five elements (see below). Lynch's view of place implies that linkages exist between places based on a person's experience with those locations, creating a tapestry of meaning imposed upon an urban landscape. Similarly, (Tuan 1977) differentiates place from space based on the familiarity a person might have of the former; as a space becomes more familiar, more intimately known, it is transformed into a place. `If space is movement, then place is pause' (Tuan 1977, p.6).

Maps are one tangible way of conceptualizing and representing place and space. Modern maps, such as atlas and road maps, are mass-produced for a consumer market, and are the result of painstaking work done by experts in the fields of cartography, air photo interpretation, statistics, and other disciplines. One of the goals of a modern map is to communicate an objective representation of place that is accurate, useful and that conveys a sense of that place (Taylor 2009). For instance, a map of a city may convey a sense of place by rendering a collection of place names (buildings, roads, plazas, etc) overlaid on a road network, on top of topographic features.

While a map-reader may get a sense of that place, via those representations, the map does not convey the deeper understanding of place that comes from everyday experience, meanings and associations that a resident may possess. In fact, maps may distort a local knowledge by misrepresenting the meaning of place as reflected by, for instance, place names (Frank 2000). In this instance, expert knowledge of map-making is not enough to produce maps that convey an accurate sense of place. The missing ingredient is the experience of a place that can only be gleaned by visiting, or perhaps by interacting with someone who has intimate knowledge of it.

One outcome of this central and authoritative communication of place, via consumer maps, is that citizens are relegated to the role of consumers to be consulted by experts (i.e. urban planners, academics) in their quest to understand what a place means to people. Tools such as cognitive mapping (Kitchen 2009) and mental maps (Lynch 1960) are traditionally utilized to mine these location data from individuals. Tversky (1993) defines cognitive mapping as the process of mentally acquiring, storing, recalling, and decoding metric information relative to location. Mental mapping is the non-metric capture of spatial relations among elements, allowing reorientation, spatial inference and perspective taking (Downs 1977). The importance of these concepts is that they rely on an individual's non-expert or lay, understanding of space. Indeed, the academic notion of Naïve Geography (Egenhofer 1995) is predicated on a `common sense' understanding of geography, where the focus is largely on the non-expert.

The descriptions of place gleaned from the cognitive, mental or naïve are the result of an individual process of understanding. While there are shared map elements between people (e.g. most people will recognize a 'cross' as being the location of a church on a map), these shared elements do not represent a shared experience or the basis of a common understanding within a community. Within this context, there are a variety of ways that an individual or community can understand place, maps being one.

References

Devine-Wright, P. and Lyons, E.: 1997, Remembering Pasts and Representing Places: The Construction of National Identities in Ireland, Journal of Environmental Psychology 17, 33–45.

Downs, R. M. and Stea, D.: 1977, Maps in Minds. Reflections of Cognitive Mapping., Harper and Row.

Egenhofer, M. J. and Mark, D. M.: 1995, Naive Geography, in A. Frank and W. Kuhn (eds), Spatial Information Theory: A Theoretical Basis for GIS, International Conference COSIT ’95, Vol. 988 of Lecture Notes in Computer Science, Springer, Semmering, Austria, pp. 1–15.

Gieryn, T.: 2000, A Space for Place in Sociology, Annual Review of Sociology 26, 463–496.

Harrison, S. and Dourish, P.: 1996, Re-place-ing Space: The Roles of Place and Space in Collaborative Systems, Proceedings of the 1996 ACM conference on Computer Supported Cooperative Work, CSCW ’96, ACM, New York, NY, USA, pp. 67–76.

Kitchen, R. M.: 1994, Cognitive Maps: What they are and why we study them?, Journal of Environmental Psychology 14(1), 1–19.

Lynch, K.: 1960, The Image of the City, The MIT Press.

Lynch, K., Banerjee, T. and Southworth, M.: 1995, City Sense and City Design: Writings and Projects of Kevin Lynch, MIT Press.

Relph, E.: 1976, Place and Placelessness, Pion.

Tuan, Y.: 1977, Place and Space: The Perspective of Experience, University of Minnesota Press.

Tversky, B.: 1993, Cognitive Maps, Cognitive Collages, and Spatial Mental Models, in A. U. Frank and I. Campari (eds), Spatial Information Theory: A Theoretical Basis for GIS, Proceedings COSIT ’93, Vol. Lecture Notes in

Computer Science, Springer: Berlin, pp. 14–24.

Zook, M. A. and Graham, M.: 2007, Mapping Digiplace: Geocoded Internet Data and the Representation of Place, Environment and Planning B 34(3), 466–482.