GIS
final ELECTIVE
assignment
DIVYA CHAND Urban Fellow IIHS
CONTEXT From my home in the north of Nagpur, I would have to travel to the Western corner of the city each day to catch a bus to my workplace. I would drive on my two-wheeler to this bus stop, a distance of 6 to 7km. The path I’d choose to take each day would differ. A number of interconnecting roads in the city lead to the same destination, and I would instinctively make a separate set of decisions each day to get there. While these decisions were informed by the time it’ll take to reach and the amount of traffic expected or encountered on the way, they were in no way restricted by these factors. These seemingly random commute decisions each day were driven by the nature of the city space and my perceptions of it.
INTENT The intent of this exercise is to attempt and understand the factors that affect commuting patterns in a city, beyond what GPS applications would calculate(time and distance). These factors could be that are: • Completely personal to the commuter such as: Places of memory, familiarity, chores on the way, mood • Implicit in the spatial quality of the roads: Greenery, traffic, sense of safety, amount of lighting, visual pleasantness • Dependent on the mode of transport: cars/bikes/bi-cycles/ pedestrians • Weather on that day etc. On each given journey, one or a combination of these factors might be activated. For people not familiar to and comfortable with the city, especially when they are not cocooned in 4 wheeler capsules, it would be beneficial to have these factors considered when a certain path is suggested to travel. Not only would this data make for pleasant drives, it would also acquaint people to points of interest in their city, and make for slightly (if not much more) happier city dwellers.
process
HOME
While the vision is to create a dynamic, city level data-set of said factors, here it is tried out with one sample journey. Different spatial elements of the commute are mapped and their attributes assigned factors for consideration. A lot of the factors in consideration are qualitatively ranked according by the commuter. (Ideally-crowd sourced data in relation with commuter’s personal preferences; Here- author’s experiential perceptions). BUS STOP
DATA SET Points of Interest
Place Name Why (Nature of interest) Landmark
Eatery Architecture Road Feature Shop Observable (Visible from the road) Y N fscntnfctr (Rating of Fascination) 12345 Stopchance (Chance of Pulling over) 12345
Built Up Spaces
Landuse (Land Usage)
Residential Market(mixed) Market Institutional Grain (Rating of grain of built up) 12345
Green Spaces
Roads
Land Ownership
Public Private Institutional Government Access Accessible Veh Access (Vehicular Access in area) Inaccessible Y (yes) or N (no) Restricted Ped Access (Pedestrian Access in area) Nature Y (yes) or N (no) Open or Mixed Covered
Name of road Deg Traffic (Rating of Traffic Density)
Sticky Edge (Rating of activity on street edges)
Lighting (Street light functioning)
12345
12345
Y (yes) or N (no) or S(sometimes) Greenery (Rating of trees present) 12345
Traffic Signals
Name (of square) Works?(Traffic light functioning)
WaitTime (Usual amount of time taken)
Y (yes) or N (no) or S(sometimes)
12345
MAPPING HOME
BUS STOP
• Google Earth screen-shot of relevant area is Geo referenced in QGIS. • Data is digitized for all possible roads that can be crossed to travel between two points along with all traffic intersections, points of interests and green and built up spaces encountered. • Base Map (as in the next page) is formed. • The page after shows the base Attribute Data created.
HOME
BUS STOP
BASE
MAP
Attribute Data TRAFFIC crossings
Roads
Attribute Data bUILT UP SPACES
GREEN SPACES
Points of Interest
query 1
Method
The morning weather is pleasant and I am early for the bus. I would like to drive on roads with more greenery, less traffic, and also stop over at a park for some time. I would like for this park to be adjacent to the road so I do not have to go out of the way and get late.
Select roads with Greenery Rating >3 and Traffic rating <3. Make new layers to highlight these. Create a 20m buffer around roads, and use Spatial Query to identify Accessible green spaces on the way.
HOME
BUS STOP
query2
Method
A friend is visiting the city and has decided to accompany me to work. I want to show her some iconic places in Nagpur through the drive and have food on the way. I would want the place we stop to eat at to be in a pleasant green locality.
Select Points of Interest with Fascination factor >2 and Observable from the road Make new layers to highlight these. Create a 20m buffer around eateries, and use Spatial Query to retain those that are close to green spaces.
HOME
BUS STOP
query3
Method
It is late evening and it is raining heavily and I need to head back home on my two wheeler. I would want to be driving in streets that are well lit up. I will try to avoid traffic signals with longer waiting times. I would also prefer to drive through roads around market areas so that I can take shelter in shops etc. in case the intensity of rain increases and driving gets difficult.
Select roads with Lighting Rating >3 (to use) and Identify Traffic signals wit wait time >3 (to avoid). Make new layers to highlight these. Create a10m buffer around Markets and Market(mixed) areas , and use Spatial Query to identify roads that fall in this zone.
READING These maps highlight the preferred paths to take and place that can be visited, in the specific situations. They fall short in providing the exact most feasible path to reach the destination given all these concerns. This can be achieved through google-maps like app/software in which data of similar nature is fed and continuously processed.
THANK YOU