That’s because the text search feature is looking for the names particular datasets, not specific location names. So the best way to search for neighborhoods is by using the Map filter, not the text search feature. Just zoom into your neighborhood on the map and Data Engine will return datasets that have coverage in that area.
Do you have a question on how to get the most out of Data Engine? This Help page is organized by area and activity. If you don't see your question answered here, please visit the Feedback page and submit your question. We will respond as soon as possible.
Here are some basic questions to ask as you start to look for data sets to support your work:
- What question are you trying to answer? Knowing this will help you narrow down the answers to the next questions. For example: What neighborhood has the greatest number of children ages 10-14? or What school has the best performing 5th grade math students?
- What geographical area are you interested in researching? Use the map box in the search criteria to locate the area you need information.
- What topic (or category) are you interested in researching? Select one or more categories in the search criteria to return only the data sets related to that topic(s).
- Is there a particular data source you would like to use? Select one or more sources in the search criteria to return only the data sets created by that source.
There is the chance Data Engine does not have the data set you are interested in. Please suggest a data set through the Feedback/Contact form.
There are several ways to search for data in Data Engine.
- You can search for data sets through the direct text search box which searches for data by specific subject matter keywords, such as ‘demographics’, ‘K-12 education’ etc.
- The Location map allows you to locate (data sets associated with specific areas, neighborhoods, etc., you are interested in) the area you are interested in finding relevant data sets.
- The Category filter allows you to select one or more categories or topics from which the system will return results.
- The Source filter allows you to select one or more data sources to return results.
- The Year filter allows you to select one or more years to return results.
- Selecting from one or more of the search filters will help you narrow your results.
Yes, you can contribute your own data to Data Engine – and we encourage it! In an effort to make previously unavailable data more accessible to individuals and organizations who rely on data to create community change, Data Engine encourages the contribution, sharing and exchange of data through this open source platform. To ensure that your contributed data can be mapped in Data Engine’s visualization tool, we do have specific data type structure requirements. To learn more about these and other requirements visit the Uploading Data section on the Help page. The Upload page also has inline tool tips to help guide you through the process. If you still have questions please visit the Feedback page, and we will respond as soon as possible.
A huge benefit of Data Engine is the ability to turn data that has geographic references into powerful chart or map visualizations to make data more translatable and understandable. To ensure that your contributed data can be mapped in Data Engine’s visualization tool, your data will need to conform to the existing shapefile unique identifiers (or GEOID's) that are currently in Data Engine. The following are examples of the shapefile GEOID's that your data type structure must conform to in order for your contributed dataset to be mapped in Data Engine's visualization tool.
Census Block - "080010085351020"
Census Block Group - "080710004003"
Census Tract - "08051963900"
Census County - "08069"
Zip Code - "80023"
School (code) - "1901"
School District (code) - "0520"
Neighborhood (Denver only) - "Capitol Hill"
City - "Name"
State - "08"
Data Engine's upload data form collects pertinent identification metadata to display for other users, for both tabular and spatial data. The form was influenced by the Federal Geographic Data Committee (FGDC) standards, and adapted to ensure adequate information is collected without being too onerous for the contributor. While the FGDC standards were developed for spatial data, most data (stored in a tabular format) ultimately has geographic reference and is why Data Engine referenced the FGDC identification portion of the standard, as well as allowing for entity and attribute information collection through data dictionary and lookup table uploads. The standards document can be found here: http://www.fgdc.gov/standards/projects/FGDC-standards-projects/metadata/....
Data Engine administrators will monitor the user contributed spatial data to determine if it will be appropriate in the future to require additional FGDC standards around data quality, spatial reference, and distribution information.
As long as your data set represents Colorado or any part of it you can contribute the data. The caveat is your data would be limited to creating chart or graph visualizations. If your data is NOT tied to a specific place in Colorado it will not be able to be visualized in a map.
Please select "Other" as the data category as the data category for your dataset and identify the suggested name of the category in the description block. A Data Engine administrator will follow up with you to verify the new category prior to publishing the dataset.
Each contributed dataset must only reference one unit of analysis. If you have a dataset with more than one unit of analysis it will need to be separated prior to uploading.
General Data & Visualization
Data is information that is collected and presented in an easily digestible way. The information can take many forms; photographs, statistics, locations, stories, financial, history - you name it really!
What does data look like? Data can be presented in many ways, such as graphs and charts, to more creative and artistic ways. See the video below for a very creative and easy to understand example of visually presenting data.
Where does data come from? It can come from any source! What are you interested in? Chances are there will be data somewhere for that (though it might need to be collated!). Do you want to know how your council spends money? Do you want to know how many people used your local library in February 2008? Perhaps you are interested in health statistics, or how many photographs there are of your town taken in 1950? Whatever your interests there is most likely a data set to bring it to life.
Who can benefit from data? Anyone. From the government, schools, students, health bodies, corporations, small businesses, bloggers, and individuals who would like the data for personal or research reasons.
Why does data matter to communities? Because a thriving democracy hinges on full citizen engagement, which stems from data-supported knowledge. Data can reveal hidden truths, identify critical patterns and drive powerful insights and decisions that shape the quality of life we want in our communities. In this way, data is a powerful tool for community change
This blog sums it up nicely: http://abitmoreofkaren.blogspot.com/2011/02/understanding-data-what-is-d...
Here are a few links to some sources to help you learn more about creating effective visualizations. If you know of some other sources, please let us know by using the Feedback page.
Visualize This: http://www.youtube.com/watch?v=mkEXx7sDXAI
Knight Digital Media Center tutorials: http://multimedia.journalism.berkeley.edu/tutorials/
Data Stories: http://www.thinkwithgoogle.com/quarterly/creativity/data-stories.html
What is Data Visualization?: http://www.youtube.com/watch?v=YaGqOPxHFkc
A good visualization should fulfill these requirements: http://www.theusrus.de/blog/fundamentals-whats-the-story/
Data Visualization 101: http://www.hunterdehaven.com/2012/07/05/fundementals/
Here are a few links to some sources to help you learn more about data. If you know of some other sources, please let us know by using the Feedback page.
What is Data?: http://www.mathsisfun.com/data/data.html
Data Wrangling Handbook: http://handbook.schoolofdata.org/en/latest/index.html
Dataset basics: http://www.youtube.com/watch?v=js1s_tDUQmE