Data Visualization with Maps/Coding
Budget: $10 – $30 USD
I'm looking for a skilled coder who can assist me with a project involving the creation of maps for data visualization purposes. The primary focus of this project is to visualize geographic distribution data on maps.
Ideal skills for this role include:
- Proficiency in coding, particularly in languages commonly used for data visualization such as Python or R.
- Experience with mapping software or libraries (e.g. GIS software, Leaflet.js, D3.js)
- Strong understanding of data visualization principles.
- Ability to convey complex data in an accessible and visually appealing way.
-Arc gis
Please provide examples of similar projects you've completed in your bid.
In this assignment, you will perform a hypothetical consulting project on bicycle access for the Town of Chapel Hill.
The Town of Chapel Hill is certified by the League of American Bicyclists as a silver-level bicycle friendly community. However, Town residents and officials know that more can be done to encourage bicycle use. In particular, the town has many greenways that are useful for recreation, but high-speed roads cut off different parts of town from each other, making cycling for transportation difficult.
The Towns of Chapel Hill and Carrboro have hired you as a consultant to evaluate the current bicycle network and recommend improvements. In particular, they are interested in improving bicycle connectivity to the University, as parking there is inadequate for all employees and students to drive, and cycling could provide a viable alternative for many commuting from within the Chapel Hill/Carrboro area.
The Town has provided you with a network dataset derived from OpenStreetMap that represents a network of the streets and bicycle paths in the Chapel Hill area (available on Canvas). This network is already mostly usable for routing—it is noded (i.e. the lines are broken at every intersection where you can turn from one street to another), although no additional data cleaning has been undertaken. If you notice connectivity problems with the network during the course of your analysis, feel free to correct them. You can download the data here Download download the data here.
The Town would like you to use something similar to the Level of Traffic Stress methodology defined in the article “Network Connectivity for Low-Stress Bicycling” by Peter Furth, Maaza Mekuria, and Hilary Nixon, which you can find through the UNC Library. While bicycles are legally allowed on almost every street in Chapel Hill, most riders will find many Chapel Hill streets unsafe or uncomfortable. The Level of Traffic Stress methodology assigns stress levels to each road, and then evaluates cycling connectivity based on the subset of the street network that meets low-stress standards. There are four levels of traffic stress, 1-4. Level 1 corresponds to places where it is safe for children to ride. Level 2 corresponds to places where most adults will be comfortable. Level 3 indicates places that have bicycle facilities such as lanes that are close to high-speed traffic. Level 4 indicates places that are higher-stress than that – e.g. mixed traffic on a highway. The Town wishes you to perform this analysis based on a Level 2 network – i.e. evaluating bicycle connectivity using a network with only streets and paths that are Level 1 or 2.
You need to provide the Town with a report explaining your findings and policy recommendations. The report you submit should be in narrative format, describing your analysis and answering the questions below in paragraph form. Maps should have a scale bar, legend, and attribution. Maps should be numbered as figures and referenced in the text.
The paper by Furth, Mekuria, and Nixon outlines the criteria for level of traffic stress levels, based on speeds, lane widths, parking, and frequency bike lane blockage due to deliveries, double-parking, etc. The network the Town provided you does not have all of this information. It does have the following columns:
bike_ln – yes if road has a bike lane highway – the roadway classification. Due to OpenStreetMap being a British project, some of the terms may not perfectly map onto US terms – you can see a list of the values on the OpenStreetMap wiki. Note that this will also identify the greenways/bike paths, which should have LTS 1 since they have no motor traffic. spd_lm_ – speed limit of the road in miles per hour, though many roads have missing data. The Chapel Hill townwide speed limit is 35 on roads where there is no posted speed, but you may wish to assume a lower speed for small neighborhood streets that may either be posted at a lower speed but not reflected in the data, or too small and curvy for drivers to travel at 35 mph. lanes – the total number of lanes on a street – e.g. a street with two lanes in each direction would be recorded as having four lanes. Missing for many streets.
Using these four variables, develop a methodology to roughly classify the roads in the network by whether they are LTS 2 or less. You won’t be able to reproduce the methodology described by Furth, Mekuria, and Nixon exactly, since you don’t have all the data they used, but use your best judgement. You will probably not be able to account for the LTS of crossing of larger roads since you don’t have information on traffic light locations. Document the rules you used. [1 point].
Create a new column in the data indicating whether streets are LTS 2 or not. You can use AND and OR in the QGIS expression editor to combine conditions for multiple fields. Keep in mind order of operations for AND and OR; AND is evaluated before OR. For example, highway = "primary" AND spd_lm_ < 45 OR bike_ln = "yes" will select primary roads with a max speed below 45 mph, as well as all roads (primary or not) with bike lines. If you wanted to select primary roads with either max speed below 45 or bike lanes, but not select an non-primary roads, you would need to use parentheses: highway = "primary" AND (spd_lm_ < 45 OR bike_ln = "yes")
Include the expression(s) you used to determine which streets were LTS 2 or lower in an appendix of your writeup [2 points].
Make a map highlighting the streets that are LTS 2 or less. Include street names. [1 point].
Four miles is a fairly reasonable biking commute. Perform two service area analyses of the areas within four miles network distance from the Old Well, one on the LTS 2 network and one on the complete network (a shapefile of the Old Well location is available in the data you downloaded). Make sure you account for one-way streets. Map them both on the same map. How much does lack of safe cyclist accommodation on many of the roads in Chapel Hill limit the ability to commute to campus by cycling? [2 points]
To avoid building new parking decks, UNC is willing to set aside some money to improve cycling connectivity in the town. Recommend one additional bike lane (on an existing street, change a LTS 3 or 4 street to LTS 2) and one additional bike path (not on an existing street, draw in a new link in the network) to improve connectivity between UNC and the town. It’s okay if these projects are far from campus as long as they improve the ability of people to cycle to campus on the LTS 2 network.
Perform a service area analysis of the modified network including your improvements, to show the areas that would be within a 4 mile bike ride of the Old Well.
Make sure you test to make sure your new paths and lanes were connected the way you would expect. When drawing, I recommend enabling the snapping toolbar (View -> Toolbars -> Snapping Toolbar), and snapping to line endpoints only (dropdown to the left of the snapping distance control). I recommend using the shortest path tool to make sure QGIS actually uses the facility. (If your proposed facility connects to the rest of the network in multiple places, you may want to use multiple shortest path analyses to make sure that all connections are connected.) If your path connects to the network in more than two places, you will need to draw it as multiple lines so that the intermediate access points are useable.
Make a map comparing the bikeable area using the current LTS 2 network and the bikeable area with your improvements. Highlight your new path and lane on the map. [4 points
Ideal skills for this role include:
- Proficiency in coding, particularly in languages commonly used for data visualization such as Python or R.
- Experience with mapping software or libraries (e.g. GIS software, Leaflet.js, D3.js)
- Strong understanding of data visualization principles.
- Ability to convey complex data in an accessible and visually appealing way.
-Arc gis
Please provide examples of similar projects you've completed in your bid.
In this assignment, you will perform a hypothetical consulting project on bicycle access for the Town of Chapel Hill.
The Town of Chapel Hill is certified by the League of American Bicyclists as a silver-level bicycle friendly community. However, Town residents and officials know that more can be done to encourage bicycle use. In particular, the town has many greenways that are useful for recreation, but high-speed roads cut off different parts of town from each other, making cycling for transportation difficult.
The Towns of Chapel Hill and Carrboro have hired you as a consultant to evaluate the current bicycle network and recommend improvements. In particular, they are interested in improving bicycle connectivity to the University, as parking there is inadequate for all employees and students to drive, and cycling could provide a viable alternative for many commuting from within the Chapel Hill/Carrboro area.
The Town has provided you with a network dataset derived from OpenStreetMap that represents a network of the streets and bicycle paths in the Chapel Hill area (available on Canvas). This network is already mostly usable for routing—it is noded (i.e. the lines are broken at every intersection where you can turn from one street to another), although no additional data cleaning has been undertaken. If you notice connectivity problems with the network during the course of your analysis, feel free to correct them. You can download the data here Download download the data here.
The Town would like you to use something similar to the Level of Traffic Stress methodology defined in the article “Network Connectivity for Low-Stress Bicycling” by Peter Furth, Maaza Mekuria, and Hilary Nixon, which you can find through the UNC Library. While bicycles are legally allowed on almost every street in Chapel Hill, most riders will find many Chapel Hill streets unsafe or uncomfortable. The Level of Traffic Stress methodology assigns stress levels to each road, and then evaluates cycling connectivity based on the subset of the street network that meets low-stress standards. There are four levels of traffic stress, 1-4. Level 1 corresponds to places where it is safe for children to ride. Level 2 corresponds to places where most adults will be comfortable. Level 3 indicates places that have bicycle facilities such as lanes that are close to high-speed traffic. Level 4 indicates places that are higher-stress than that – e.g. mixed traffic on a highway. The Town wishes you to perform this analysis based on a Level 2 network – i.e. evaluating bicycle connectivity using a network with only streets and paths that are Level 1 or 2.
You need to provide the Town with a report explaining your findings and policy recommendations. The report you submit should be in narrative format, describing your analysis and answering the questions below in paragraph form. Maps should have a scale bar, legend, and attribution. Maps should be numbered as figures and referenced in the text.
The paper by Furth, Mekuria, and Nixon outlines the criteria for level of traffic stress levels, based on speeds, lane widths, parking, and frequency bike lane blockage due to deliveries, double-parking, etc. The network the Town provided you does not have all of this information. It does have the following columns:
bike_ln – yes if road has a bike lane highway – the roadway classification. Due to OpenStreetMap being a British project, some of the terms may not perfectly map onto US terms – you can see a list of the values on the OpenStreetMap wiki. Note that this will also identify the greenways/bike paths, which should have LTS 1 since they have no motor traffic. spd_lm_ – speed limit of the road in miles per hour, though many roads have missing data. The Chapel Hill townwide speed limit is 35 on roads where there is no posted speed, but you may wish to assume a lower speed for small neighborhood streets that may either be posted at a lower speed but not reflected in the data, or too small and curvy for drivers to travel at 35 mph. lanes – the total number of lanes on a street – e.g. a street with two lanes in each direction would be recorded as having four lanes. Missing for many streets.
Using these four variables, develop a methodology to roughly classify the roads in the network by whether they are LTS 2 or less. You won’t be able to reproduce the methodology described by Furth, Mekuria, and Nixon exactly, since you don’t have all the data they used, but use your best judgement. You will probably not be able to account for the LTS of crossing of larger roads since you don’t have information on traffic light locations. Document the rules you used. [1 point].
Create a new column in the data indicating whether streets are LTS 2 or not. You can use AND and OR in the QGIS expression editor to combine conditions for multiple fields. Keep in mind order of operations for AND and OR; AND is evaluated before OR. For example, highway = "primary" AND spd_lm_ < 45 OR bike_ln = "yes" will select primary roads with a max speed below 45 mph, as well as all roads (primary or not) with bike lines. If you wanted to select primary roads with either max speed below 45 or bike lanes, but not select an non-primary roads, you would need to use parentheses: highway = "primary" AND (spd_lm_ < 45 OR bike_ln = "yes")
Include the expression(s) you used to determine which streets were LTS 2 or lower in an appendix of your writeup [2 points].
Make a map highlighting the streets that are LTS 2 or less. Include street names. [1 point].
Four miles is a fairly reasonable biking commute. Perform two service area analyses of the areas within four miles network distance from the Old Well, one on the LTS 2 network and one on the complete network (a shapefile of the Old Well location is available in the data you downloaded). Make sure you account for one-way streets. Map them both on the same map. How much does lack of safe cyclist accommodation on many of the roads in Chapel Hill limit the ability to commute to campus by cycling? [2 points]
To avoid building new parking decks, UNC is willing to set aside some money to improve cycling connectivity in the town. Recommend one additional bike lane (on an existing street, change a LTS 3 or 4 street to LTS 2) and one additional bike path (not on an existing street, draw in a new link in the network) to improve connectivity between UNC and the town. It’s okay if these projects are far from campus as long as they improve the ability of people to cycle to campus on the LTS 2 network.
Perform a service area analysis of the modified network including your improvements, to show the areas that would be within a 4 mile bike ride of the Old Well.
Make sure you test to make sure your new paths and lanes were connected the way you would expect. When drawing, I recommend enabling the snapping toolbar (View -> Toolbars -> Snapping Toolbar), and snapping to line endpoints only (dropdown to the left of the snapping distance control). I recommend using the shortest path tool to make sure QGIS actually uses the facility. (If your proposed facility connects to the rest of the network in multiple places, you may want to use multiple shortest path analyses to make sure that all connections are connected.) If your path connects to the network in more than two places, you will need to draw it as multiple lines so that the intermediate access points are useable.
Make a map comparing the bikeable area using the current LTS 2 network and the bikeable area with your improvements. Highlight your new path and lane on the map. [4 points