In a similar idea to http://flowingdata.com/2010/05/25/world-atlas-of-flickr-geotaggers/ and http://flowingdata.com/2010/05/25/map-of-where-toursists-flock/ I would like to extract OSM planet data, say the coordinates of schools, and overlay them on a map so that when zoomed out you would get a kind of heatmap of where the world's schools are. Surely this should be relatively easy but I can't find a tool to do it. OSM planet does give coordinates of points by point type, after all, doesn't it?
There is a tool Osmosis written in JAVA that can be used for advanced data manipulation in OSM format.
For instance you could parse planet.osm and only extract hospitals (geometry and attributes) using:
osmosis --read-xml input.osm --tag-filter accept-nodes amenity=hospital --write-xml output.osm
Actually you could pipe output directly to osm2pgsql command and load data to PostGIS database, or convert osm data to format that is more readable by common GIS software.
I'm not sure how long will it take to parse planet.osm, but you could start with smaller area and work your way up. Also, you can use osmosis to extract a smaller area of interest from planet.osm, if daily prepared extracts don't satisfy your needs.
OpenStreetMap (OSM)'s planet.osm is commonly packaged as XML, and thus its components can be parsed and extracted however you wish. So use your favorite programming language or software tool to parse the data, and, from there, package and place the data on your own map. For instance, if you prefer ArcGIS and don't want to code, you could use tools like http://www.polygongis.com/OSMTranslator.aspx or the http://esriosmeditor.codeplex.com/ to extract the point data of interest. Using those tools (that limit the areal extent of data extracted at any one time), you'll need to separate the world into smaller chunks and merge them back together on your own. You could then do a surface interpolation using your method of choice: IDW, kriging, or whatever.
Be sure to credit OpenStreetMap data per its license.
The OpenStreetMap wiki has a great deal of useful information about the OSM data structure. For instance, see:
Since there are many, many tools to visualize geographic data (and OSM data is easy to transfer into common geographic data types; for instance, http://downloads.cloudmade.com/ offers OSM extracts in numerous formats for free), it would be helpful to enumerate what computational or personal skillsets you have available or would like to use.
A more "standard" GIS approach would be:
- Download a POI shapefile from Cloudmade
- Generate a heatmap raster in a desktop GIS using a kernel density filter
- Export this raster into your favourite map server/tiling package
- Serve on the WWW.
But perhaps this approach is a little too 2005 for the cool web-mapping crew here :)
Try the Open Source code just released from OLHeatmap.
- OSM Base Map
- OSM Data (Points selected by type).
- OpenLayers with source code.
What is OLHeatmap ? "Making Open Street Maps even hotter!" [Generates HeatMaps] Blog post & Video http://mapperz.blogspot.com/2010/08/open-layers-heatmaps.html
Credit to Felipe Barriga (http://blog.felipebarriga.cl/)
Generate some heatmaps here: from OSM data (not schools) but gives you an idea.
Not saying this is the perfect solution, but looking into it might help save hours of coding
Data visualisation from OSM planet - Geographic Information Systems
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An Old Idea Retooled
The bird surveys that informed the Audubon report have a surprisingly long history. The Christmas Bird Count dates back to 1900, when the ornithologist Frank Chapman persuaded hunters to count live birds instead of shooting them for sport. With tens of thousands of amateurs and many regional chapters contributing millions of observations each year, it is a complex undertaking.
The Christmas Bird Count dates back to 1900, when the ornithologist Frank Chapman persuaded hunters to count live birds instead of shooting them for sport.
The next generation of citizen science platforms could be represented by the website and mobile app iNaturalist. Developed by graduate students at the University of California, Berkeley, and now operated by the California Academy of Sciences, iNaturalist passed its millionth contribution last fall. (Full disclosure: iNaturalist has partnered with National Geographic.) Registered users take a picture of a plant, animal, or bug the smartphone then assigns a date, time, and location the report is then uploaded. Once three designated experts agree on what the picture shows, it becomes a "museum grade" observation. Eventually the information is uploaded into the Global Biodiversity Information Facility, a data bank.
Unlike other citizen science hubs such as Cornell University's eBird and Zooniverse, the iNaturalist website isn't oriented strictly toward data visualization, but given the quantity of the submissions, visualization plays an important role. The iNaturalist website has a map showing all of the observations made since its inception eBird has a similar map showing contributions in real time.
Data visualization and geographic information systems 7
As an IT manager, Please refer to the attached document and discuss the ways you would use the materials in the attachment to communicate IT information to other departments. Use research from 3 academically reviewed journal articles to substantiate the reading material from attached document.
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Data visualisation from OSM planet - Geographic Information Systems
Acetate is a set of stylesheets that are designed specifically for geographic data visualization. It includes several layers: topographic basemap, hillshading, roads, placenames. These layers can be used individually in combination in layering with thematic data, or composited together into a single image.
You can use Acetate tile layers by using the following template urls in a web map
- Basemap (preview)
- Basemap, Hillshading (preview)
- Basemap, Hillshading, Placename labels (preview)
- Roads, Placename labels (preview)
- Roads (preview)
- Placename labels (preview)
- Hillshading (preview)
Acetate is built upon the Tilestache, Mapnik projects and it uses a combination of PostGIS and Shapefiles to store spatial data. The data used is OpenStreetMap, Natural Earth and some custom data sources. The two custom data sources are created through a process of “simulated annealing.”
In order to get started with the data install PostGIS and download the OpenStreetMap Planet File. You’ll need to use OSM2PGSQL to import it. The process of importing for the whole world can take a while, if you only need a specific country you might want to grab a country specific extract from GeoFabrik. To get the coastline information you’ll need to get the data from Natural Earth.
The custom data is for place names and simplified motorways. You can download the place name shapefiles from here. The simplified motorways is a SQL script that should be run after the OSM Planet is imported.
To get yourself going install Tilestache from Github. From the README Mapnik is listed as an optional dependency but for our purposes you need it.
At the moment we give you all the pieces to roll your own, though look for a full tutorial in the coming weeks.
This step is about just placing the acetate project into a web accessible place. Drop them the project into a web dir and start making tiles.
Applying feminist theory to mapping and GIS may seem weird, but it correlates with some of the broader issues we have been engaged in the class, including the entire idea of crowdsourcing (it’s non-hierarchical, pluralistic, empowering, and so on) as well as the web GIS technologies, which embrace some of these ideas at their core.
“Feminist Data Visualization” by Catherine D’Ignazio and Lauren F. Klein was a preface to a book that was published last year called Data Feminism. We will read and discuss the former, but I’ll include some details from the latter below.
“Feminist Data Visualization” PDF here.
Data Feminismbook information here.
Ponder these questions, look up citations, and google stuff: examples are always good:
The design team followed a user-centered design model for facilitating stakeholder involvement and designing the tool for optimal interface success, which proved successful for designing web-mapping, visualization, and data exploration projects (20&ndash22). We used the user&ndashutility&ndashusability loop developed by Roth, Ross, and MacEachren, in which we collected input and feedback on needs and designs from preterm birth researchers and stakeholders (user), prompting revisions to the conceptualization and functional requirements of the tool (utility), leading to new versions of the data visualization tool (usability) for additional evaluation by our target users, thus restarting the loop (23).
An initial draft of the visualization tool using the DELPHI platform was presented to the Fresno County Preterm Birth Initiative in early 2017. Feedback obtained from stakeholders included comments about variables to be used, geographic resolution, and the need for a more user friendly and simple site that would allow less advanced users to explore and visualize key data sets. The public-facing side of the site was presented again to the Fresno County Preterm Birth Initiative in early 2018 to obtain further input and to narrow down the key topics of interest. The design team then worked with the Fresno County Preterm Birth Initiative Shared Measures Committee, a subgroup of the Fresno initiative that focuses on data and measurement issues, over several meetings to come up with 8 topics to feature on the public-facing side of the tool. The Shared Measures Committee comprises cross-sector leaders and experts in measurement and evaluation and a mother who experienced preterm birth. This committee helps set goals, inform strategies, and establish or develop measures of progress for the Fresno initiative. The iterative feedback between the design team and the Shared Measures Committee was critical for the design of the tool and for determining how the 8 topics should be populated and visualized. Having the design team attend multiple meetings of the Fresno initiative gave additional context to challenges surrounding preterm birth, such as social disadvantage and health care access, and the need to represent such phenomena in maps in a transparent way. Further discussions highlighted how the tool needed to be easy to use for nonprofit organizations to create figures for grant applications and accessible to the public and elected officials. In addition, the tool needed to be bilingual, for both English and Spanish speakers.
The tool was presented to the public and other Fresno initiative stakeholders in July 2018 at the Fresno County Preterm Birth Initiative Forum. The purpose of the event was to convene community members and stakeholders to raise awareness about preterm birth and communicate Preterm Birth Initiative strategies, successes, and challenges. As an example, the team walked through an assessment of environmental pollution as related to preterm birth from the viewpoint of a concerned community member and then from the viewpoint of a public health researcher. Beginning with the front-end, demographics around a neighborhood of interest as related to preterm birth were examined using pie charts and histograms with linked maps (eg, Figure 1b). Moving to the environmental pollution tab, rates of ozone, PM2.5 (atmospheric particulate matter with a diameter of less than 2.5 micrometers), traffic density, and toxic release were examined in relation to 3 neighborhoods in downtown Fresno (eg, Figure 1a). Resulting figures could be used in public presentations, community discussions, or public grant applications. The demonstration then turned to the back-end site where a researcher might want to examine all possible pollution factors in association with not only preterm birth rates, but also mothers&rsquo rates of hypertension, community asthma rates, and poverty indicators by using a correlation matrix (eg, Figure 2b). The tool was presented alongside a poster outlining &ldquoUnequal Neighborhoods: Fresno&rdquo research on historical zoning and land use policies influencing health disparities. At the event, attendees were encouraged to interact with the tool and with the design team to ask questions and provide feedback. They were given a link to submit further feedback through an online survey and to stay engaged with the project. Feedback is considered and incorporated into the tool design.
Now that we have covered the setting up phase, let’s deep dive into the topic for this post.
What do you mean by interactive visualization in Jupyter? How can a widget achieve this? Most importantly, what is a widget?
These questions will inevitably pop up when you start reading this post. For those of you who are familiar, take the next section as a quick refresher.
Interactive Visualization?it simply means the ability to interact with your visualization in real-time. Take the following GIF for example. You are able to interact with the slider through a visual mean.
Jupyter Widget?they are called “special objects” that can be initialized and displayed on the notebook. They are also bidirectional, meaning that a widget does not just display, but can take in user inputs that will subsequently trigger new computation. The most important thing is that they are extensible. Jupyter widget allows for other libraries to build additional features on top of its functionality, such as, our ipyleaflet.
This paper takes a visual approach to flow-data analysis within geographical information systems, and uses spatial interaction data from the United Kingdom for illustrative purposes. As a subfield within GIS, flow mapping is something of a disciplinary laggard, despite significant advances elsewhere in the field. Therefore, the paper has three main aims. First, the intention is to show how complex spatial interaction data—frequently underutilised—can be converted into meaningful information using a GIS-based, visual approach. Second, it is hoped that the contribution will help popularise the subject and stimulate new research within spatial interaction studies and planning more broadly. The third aspect is to demonstrate that we can gain a better understanding, and knowledge of, complex spatial networks through a visual analytics approach to information generation. The paper begins by exploring some key developments in the presentation of flow data. The main body of the paper is comprised of five key geovisualisations which focus on identifying the various patterns of spatial interaction in the United Kingdom. Finally, some conclusions are drawn and direction for future development are highlighted.