Kinder Institute for Urban Research’s Post

#SaveTheDate! Join our May 9 #webinar to learn how to use our Census Business Builder #DataTool through unique data stories from our subject matter experts. Learn the steps for creating a data-driven business plan and get a preview of future data releases. ➡️ https://lnkd.in/dxQwGAyz

Don't miss out on your chance to see the most recent data for demographic and economic shifts in Nebraska! The last day to register for Part One of CPAR's Data and Research Series for Community Impact is next Wednesday, September 18! Learn more and register 👉 https://lnkd.in/gyZQFjHJ #unocpar #NebraskaByTheNumbers #NebraskaData #DataandResearchSeries #DataSeries

Check out our video tutorial to learn how to map #CensusData from the #2020Census Supplemental Demographic and Housing Characteristics File on our main data platform. Watch ▶️ https://lnkd.in/e8PtiCTt #ExploreCensusData

How can you find out more about your neighborhood? If you’re on the NDSC map, you’ll first need to find it. But where is that neighborhood again? Or rather, what neighborhood am I even in? Systems & Data Manager Elly Schoen shows a few beginner and pro tips on quickly finding LA County neighborhoods to grab the data you need. #data #lacounty #losangeles

54701 Are zip codes a useful zip file of data? Attached to any of my records, what could a data scientists determine about me if my responses were tied to other info about 54701. Adding US people's zip codes as part of a study can lead to MORE insights than possible from the limits of an individual survey response or interview. Surveys are reduced by MANY questions all captured in one datapoint-- someone's origin zip code Zip codes are limited, as pointed out by RWJF research in their Child Opportunity Index, which favors census tract Many individuals could feel excluded if they don't identify with a zip code where they grew up And yet.... datasets and research of social issues could be significantly enhanced if zip code-based data was used more often. Have you seen examples of research that levers zip codes to extrapolate findings? RWJF COI and Equity Quotient are two, linked in comments Pic from our visits back home with family this summer in front of the 2 bedroom house we lived in until I turned 10 back in 54701 Eau Claire, WI.... why does the backyard look so small to me now!? 😅😅

Check out this video tutorial to learn how to access #2020Census Supplemental Demographic and Housing Characteristics File data and compare it to data from the #2010Census. ▶️ https://lnkd.in/e9hTNtaK

Demographic Data Analyzer with Pandas 📊 I built this project to analyze census data and extract useful information from it. It also uses several of pandas functions to process, filter, and present this data. Check it out: https://lnkd.in/de25R-SN

Another New Feature From the Census Bureau: 1. Click the Address Pointer next to the mic in the search bar. 2. Enter any address 3. View demographic, housing, and many other data for the various level of geographical area related to this address, from census tract to county, metro, state, etc. So, if you want to open a new business (e.g. restaurant) at a certain address, you should check this out.

We’ve recently updated our Looking Back Tool with the latest (2023) Census data. You can now see how the built environment has changed over the last 10 years, along with how much the population has changed. A little background…. When you curate and maintain New Zealand’s most complete and up-to-date geospatial dataset you get to see small, incremental change every day. But as we all know, over time, incremental change adds up and sometimes a bit of time, distance and perspective are needed to REALLY see how much has actually changed. For instance, in 2014, New Zealand had a population of roughly 4.5 million and today we’re at about 5.2 million. Change of this scale requires infrastructure to match and huge areas of New Zealand’s built environment have also changed dramatically in this time. We capture all of this incremental change at NationalMap, meaning organisations that subscribe to NationalMap can easily analyse, compare and contrast all sorts of data visually. With that in mind, we built a side-by-side map comparison (our Looking Back Tool) for anyone interested in seeing this built environment change for themselves. If you were wondering any of the following: ·      How much bigger your town has got in terms of built area ·      How much bigger (or smaller!) your town has got in terms of population ·      How significant all those speed limit changes really were Then you can find all of this on our Looking Back Tool for the whole of New Zealand. https://lnkd.in/g5XyqSDM On the left of the visualisation is the NationalMap basemap and associated data from 2014, and on the right is 2024. We’ve added some presets in the drop down at the top which you can check out, or you can just pan around to find your own specific area of interest. This Looking Back Tool utilises just a tiny fraction of our Points of Interest (POI) data that includes features such as; hospitals, schools, petrol and charging stations, supermarkets, banks, railway stations and more. Feel free to share your thoughts on how you believe the world has changed since 2014 in the comments below and if you feel NationalMap data can help your business then please do get in touch. #geospatial #builtenvironment #census2023 #datafordecisionmakers

🌐 New Population Data Updated! The latest figures for country population data are now available. Stay informed with the most recent updates! Did we mention it's free? 😍 Get the IP2Location Country Information Database for free today! https://lnkd.in/gMjfdpsY #PopulationUpdate #CountryData #PopulationGrowth #NewData #Database #IPGeolocation #IPData #IPAddress #CountryPopulation #PopulationData #SoftwareDeveloper #SoftwareDevelopment #Tool