blockgrouphomevalues # Context A home purchase is among the most import decisions, and potentially risk investments, in a person's life. Their choice can reflect interest in long term gains, housing costs and, in the U.S., part of the American Dream. Analytics of home values and rental costs, however, are commonly limited to highest level geographic aggregates and broad, even annual, periods of time. This publication produces a data file shared in the Block Groups Datasets dataverse hosted on https://dataverse.harvard.edu/dataverse/blockgroupdatasets. The data is shared under a Common Commons, open source license, without warranties, share alike, non commercial and by attribution. Method This publication attempts to cast home values down to U.S. Census block group geographies, by inheriting and averaging the measures from ZIP code level estimates. On the whole, block groups with a few hundred households are considerably smaller than ZIP code areas with several thousand. In addition, the two geographies are managed by separate Federal agencies, the U.S. Postal Service and the Census Bureau, so they are inherently dissimilar. The simplest method of projection involves overlaying the two geographies, having a block group inherit the estimates of the ZIP code level that covers it. When the block group spans ZIP code boundaries, an average is appropriate, weighted by land area lying in each parent. Data Zillow is recognized as an innovator in predicting home values, serving real estate agents, home buyers, and home sellers. Their research service publishes several estimates at a ZIP code level including measures of home value (Zillow Home Value Index ZHVI) and rental costs (Zillow Observed Rent Index ZORI). The ZHVI is broken down by housing type: single family homes and condominiums. And, each of their publications has monthly frequency dating, in some cases, to 2000. Block group geographic boundariess are maintained by the US Census' TIGER (Topologically Integrated Geographic Encoding and Referencing) publication. ZIP code boundaries are not generally published, but shared from a private company, Dotlas, in various retail marketing solutions. ZIP codes, also, have long been problematic for demographic analytics. Their boundaries span counties and states, so you cannot tiethem to familar geographies including Census tracts and block groups. The Census Bureau tries to address this by using ZIP Code Tabulation Areas (ZCTAs). These are coded very much like 5 digit ZIP codes and are equal to them most of the time. When A ZIP code geography crosses a county line, though, new ZCTAs are invented to represent each side of the split area. So, while ZIP codes cannot be aggregated, ZCTAs can total into counties, states, divisions and regions. The blockgrouphomevalues dataset offers the following columns: Column Data Type Description STATEFP string The 2-digit State FIPS code of the block group COUNTYFP string The 3-digit County FIPS code of the block group TRACTCE string The 6-digit Census Tract of the block group BLKGRPCE string The 1-digit Block Group of the block group GEOID string 12 digit concatenation of State, County, Tract and Block Group codes GEOIDFQ string The 'fully qualified' GEOID with US country prefix ALAND integer The land area if the block group in square meters AWATER integer The area if the block group, covered by water, in square meters INTPTLAT float Latitude of the block groups centroid point INTPTLON float Longitude of the block groups centroid point ZIP Codes Overlaying list List of the ZIP codes that overlay the block group ZHVI All Housing Types float Zillow Home Value Index, attributed to the block group, all housing types ZHVI Single Family Homes float Zillow Home Value Index, attributed to the block group, single family homes ZHVI Condos/Coops float Zillow Home Value Index, attributed to the block group, condominiums and cooperatively owned ZORI All Housing Types float Zillow Observed Rent Index, attributed to the block group Additional Notes When the Block Group Code BLKGRPCE is '0', that block group is under water. Block groups cover the Great Lakes, for example, making a confusing visual for chloropleth maps. To support visualization, the code also uses Census definitions of cities called Combined Statitical Areas, which group counties together. The CSA for New York includes 22 counties, distinguished as Central or Outlying. The Delineation Files publication includes the geographic IDs of state and county FIPS codes in each major city. Maps of these results may be visually biased. New York City and San Francisco Bay areas have extreme housing values, but they have small land areas. Denver by contrast has higher then median housing values with very large land areas. As a result, western Colorado looks like the dominating location of home values. When more than one ZIP code overlays a block group, values are attributed by the shared land area. This assumes that housing is uniform over...

PROBLEM AND OPPORTUNITY In the United States, voting is largely a private matter. A registered voter is given a randomized ballot form or machine to prevent linkage between their voting choices and their identity. This disconnect supports confidence in the election process, but it provides obstacles to an election's analysis. A common solution is to field exit polls, interviewing voters immediately after leaving their polling location. This method is rife with bias, however, and functionally limited in direct demographics data collected. For the 2020 general election, though, most states published their election results for each voting location. These publications were additionally supported by the geographical areas assigned to each location, the voting precincts. As a result, geographic processing can now be applied to project precinct election results onto Census block groups. While precinct have few demographic traits directly, their geographies have characteristics that make them projectable onto U.S. Census geographies. Both state voting precincts and U.S. Census block groups: are exclusive, and do not overlap are adjacent, fully covering their corresponding state and potentially county have roughly the same size in area, population and voter presence Analytically, a projection of local demographics does not allow conclusions about voters themselves. However, the dataset does allow statements related to the geographies that yield voting behavior. One could say, for example, that an area dominated by a particular voting pattern would have mean traits of age, race, income or household structure. The dataset that results from this programming provides voting results allocated by Census block groups. The block group identifier can be joined to Census Decennial and American Community Survey demographic estimates. DATA SOURCES The state election results and geographies have been compiled by Voting and Election Science team on Harvard's dataverse. State voting precincts lie within state and county boundaries. The Census Bureau, on the other hand, publishes its estimates across a variety of geographic definitions including a hierarchy of states, counties, census tracts and block groups. Their definitions can be found here. The geometric shapefiles for each block group are available here. The lowest level of this geography changes often and can obsolesce before the next census survey (Decennial or American Community Survey programs). The second to lowest census level, block groups, have the benefit of both granularity and stability however. The 2020 Decennial survey details US demographics into 217,740 block groups with between a few hundred and a few thousand people. Dataset Structure The dataset's columns include: Column Definition BLOCKGROUP_GEOID 12 digit primary key. Census GEOID of the block group row. This code concatenates: 2 digit state 3 digit county within state 6 digit Census Tract identifier 1 digit Census Block Group identifier within tract STATE State abbreviation, redundent with 2 digit state FIPS code above REP Votes for Republican party candidate for president DEM Votes for Democratic party candidate for president LIB Votes for Libertarian party candidate for president OTH Votes for presidential candidates other than Republican, Democratic or Libertarian AREA square kilometers of area associated with this block group GAP total area of the block group, net of area attributed to voting precincts PRECINCTS Number of voting precincts that intersect this block group ASSUMPTIONS, NOTES AND CONCERNS: Votes are attributed based upon the proportion of the precinct's area that intersects the corresponding block group. Alternative methods are left to the analyst's initiative. 50 states and the District of Columbia are in scope as those U.S. possessions voting in the general election for the U.S. Presidency. Three states did not report their results at the precinct level: South Dakota, Kentucky and West Virginia. A dummy block group is added for each of these states to maintain national totals. These states represent 2.1% of all votes cast. Counties are commonly coded using FIPS codes. However, each election result file may have the county field named differently. Also, three states do not share county definitions - Delaware, Massachusetts, Alaska and the District of Columbia. Block groups may be used to capture geographies that do not have population like bodies of water. As a result, block groups without intersection voting precincts are not uncommon. In the U.S., elections are administered at a state level with the Federal Elections Commission compiling state totals against the Electoral College weights. The states have liberty, though, to define and change their own voting precincts https://en.wikipedia.org/wiki/Electoral_precinct. The Census Bureau practices "data suppression", filtering some block groups from demographic publication because they do not meet a population threshold. This practice...

This layer was deprecated on 12/31The layer will still be publicly available, but no longer update. Information and links on how to access the new updated feature service in ArcGIS Marketplace will be posted here soonSafeGraph is just a data company. That's all we do.Social Distancing MetricsDue to the COVID-19 pandemic, people are currently engaging in social distancing. In order to understand what is actually occurring at a census block group level, SafeGraph is offering a temporary Social Distancing Metrics product. This product is delivered daily (3 days delayed from actual).The data was generated using a panel of GPS pings from anonymous mobile devices. We determine the common nighttime location of each mobile device over a 6 week period to a Geohash-7 granularity (~153m x ~153m). For ease of reference, we call this common nighttime location, the device's "home". We then aggregate the devices by home census block group and provide the metrics set out below for each census block group.To preserve privacy, we apply differential privacy to all of the device count metrics other than the device_count.SchemaColumn NameDescriptionTypeExampleorigin_census_block_groupThe unique 12-digit FIPS code for the Census Block Group. Please note that some CBGs have leading zeros.String131000000000date_range_startStart time for measurement period in ISO 8601 format of YYYY-MM-DDTHH:mm:SS±hh:mm (local time with offset from GMT). The start time will be 12 a.m. of any day.String2020-03-01T00:00:00-06:00date_range_endEnd time for measurement period in ISO 8601 format of YYYY-MM-DDTHH:mm:SS±hh:mm (local time with offset from GMT). The end time will be the following 12 a.m.String2020-03-02T00:00:00-06:00device_countNumber of devices seen in our panel during the date range whose home is in this census_block_group. Home is defined as the common nighttime location for the device over a 6 week period where nighttime is 6 pm - 7 am. Note that we do not include any census_block_groups where the count <5.Integer100distance_traveled_from_homeMedian distance traveled from the geohash-7 of the home by the devices included in the device_count during the time period (excluding any distances of 0). We first find the median for each device and then find the median for all of the devices.Integer200completely_home_device_countOut of the device_count, the number of devices which did not leave the geohash-7 in which their home is located during the time period.Integer40median_home_dwell_timeMedian dwell time at home geohash-7 ("home") in minutes for all devices in the device_count during the time period. For each device, we summed the observed minutes at home across the day (whether or not these were contiguous) to get the total minutes for each device. Then we calculate the median of all these devices.Integer1200part_time_work_behavior_devicesOut of the device_count, the number of devices that spent one period of between 3 and 6 hours at one location other than their geohash-7 home during the period of 8 am - 6 pm in local time. This does not include any device that spent 6 or more hours at a location other than home.Integer10full_time_work_behavior_devicesOut of the device_count, the number of devices that spent greater than 6 hours at a location other than their home geohash-7 during the period of 8 am - 6 pm in local time.Integer0For data definitions and complete documentation visit SafeGraph Developer and Data Scientist Docs.For statistics on the dataset, see SafeGraph Summary Statistics.Data is available as a hosted Feature Service to easily integrate with all ESRI products in the ArcGIS ecosystem.Want More? Want this POI data for use outside of ArcGIS Online? Want POI data for Canada? Want POI building footprints (Geometry)?Want more detailed category information (Core Places)?Want phone numbers or operating hours (Core Places)?Want POI visitor insights & foot-traffic data (Places Patterns)?To see more, preview & download all SafeGraph Places, Patterns, & Geometry data from SafeGraph’s Data Bar.Or drop us a line! Your data needs are our data delights. Contact: support-esri@safegraph.comView Terms of Use

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from Georgia Department of Education to show public school enrollment and student characteristics, including gifted/special education/English learner status, absences/withdrawal, and Milestones assessment scores, for 2016, by census tract in the Atlanta region.

Attributes:

GEOID10 = 2010 Census tract identifier (combination of FIPS codes for state, county, and tract)

County = County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

Area_Name = 2010 Census tract number and county name

Total_Population_ACS_2016 = # Total population estimate, 2016 (American Community Survey)

Total_Population_ACS_MOE_2016 = # Total population estimate (Margin of Error), 2016 (American Community Survey)

Planning_Region = Planning region designation for ARC purposes

AcresLand = Land area within the tract (in acres)

AcresWater = Water area within the tract (in acres)

AcresTotal = Total area within the tract (in acres)

SqMi_Land = Land area within the tract (in square miles)

SqMi_Water = Water area within the tract (in square miles)

SqMi_Total = Total area within the tract (in square miles)

TRACTCE10 = Census tract Federal Information Processing Series (FIPS) code. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively.

CountyName = County Name

TOT_STUDENTS_ENROLLED_SCHOOL_YR = Total count of students enrolled at any time during the school year

SUBSET_STUDENTS_GRADES_PK_5 = Subset of total students - any student in grades PK-5

SUBSET_STUDENTS_GRADES_6_8 = Subset of total students - any student in grades 6-8

SUBSET_STUDENTS_GRADES_9_12 = Subset of total students - any student in grades 9-12

PCT_GRADES_PK_5 = Percent in grades PK-5

PCT_GRADES_6_8 = Percent in grades 6-8

PCT_GRADES_9_12 = Percent in grades 9-12

STUDENT_SERVED_BY_SPECIAL_ED = Student served by special education program

PCT_SERVED_BY_SPECIAL_ED = Percent served by special ed program

STUDENT_SERVED_BY_GIFTED = Student served by Gifted program

PCT_SERVED_BY_GIFTED = Percent served by gifted program

STUDENT_IS_ENGLISH_LEARNER = Student is a member of the English Learner student group (EL=Y or EL=Monitored Status)

PCT_ENGLISH_LEARNER = Percent in English Learner Student group

CT_RETAINED_STUDTS = Retained Student Count

PCT_RETAINED_STUDTS = Percent of Retained Students

CT_HOMELESS_UNACCOMP_STUDTS = Count of Homeless Students (Marked either "Homeless" or "Unaccompanied Youth" in SR)

PCT_HOMELESS = Percent homeless

CT_STUDTS_PARENT_ACTV_MILITARY = Count of students with parent(s) in Active Military

PCT_STUDTS_PARENT_ACTV_MILITARY = Percent students with parents in Active Military

CT_MID_STUDENTS_WITHDRAW_HOME = Grade 6-8 students withdrawn during school year, reason "H" (Withdrawn to Homeschool)

PCT_MID_STUDENTS_WITHDRAW_HOME = Percent of Middle School students withdrawn for homeschool

CT_HS_STUDENTS_WITHDRAW_HOME = Grade 9-12 students withdrawn during school year, reason "H" (Withdrawn to Homeschool)

PCT_HS_STUDENTS_WITHDRAW_HOME = Percent of High School students withdrawn for homeschool

CT_MID_STUDENTS_WITHDRAW_DJJ = Grade 6-8 students withdrawn during school year, reason "4" (Withdrawn to DJJ)

PCT_MID_STUDENTS_WITHDRAW_DJJ = Percent of Middle School students withdrawn to Department of Juvenile Justice

CT_HS_STUDENTS_WITHDRAW_DJJ = Grade 9-12 students withdrawing during school year with reason "4" (Withdrawn to DJJ)

PCT_HS_STUDENTS_WITHDRAW_DJJ = Percent of High School students withdrawn to Department of Juvenile Justice

CT_STUDENTS_WITHDRAW_ANY = Students withdrawn, any reason, 1 mo. after beginning school yr., 1 mo. before end school yr.

PCT_STUDENTS_WITHDRAW_ANY = Percent withdrawn, any reason, 1 mo. after beginning school yr., 1 mo. before end school yr.

STUDENTS_ABSENT_0_5_days = Absence Bracket A Student Count - Students absent 0-5 days

PCT_STUDENTS_ABSENT_0_5_days = Percent students absent 0-5 days

STUDENTS_ABSENT_6_15_days = Absence Bracket B Student Count - Students absent 6-15 days

PCT_STUDENTS_ABSENT_6_15_days = Percent students absent 6-15 days

STUDENTS_ABSENT_16_MORE_DAYS = Absence Bracket C Student Count - Students absent 16 or More days

PCT_STUDTS_ABSENT_16_MORE_DAYS = Percent students absent more than 15 days

CT_STUDTS_REC_DISCIPLINE = Count of students receiving any discipline event records during school year

PCT_STUDTS_ABS_REC_DISCIPLINE = Percent students absent receiving any discipline event

CT_STUDTS_OSS_MORE_10_days = Students assigned to Out of School Suspension for more than 10 days during school year

PCT_STUDTS_OSS_MORE_10_days = Percent students assigned to Out of School Suspension for more than 10 days

CT_STUDTS_ISS_MORE_10_days = Students assigned to In School Suspension for more than 10 days during school year

PCT_STUDTS_ISS_MORE_10_days = Percent students assigned to In School Suspension for more than 10 days

CT_GRD3_MILES_EOG_ELA_PRO_DIS = Count of Grade 3 Milestones EOG ELA Test Takers Scoring PRO or DIS

PCT_GRD3_MILES_EOG_ELA_PRO_DIS = Percent of Grade 3 Milestones EOG ELA Test Takers Scoring PRO or DIS

CT_GRD5_MILES_EOG_ELA_PRO_DIS = Count of Grade 5 Milestones EOG ELA Test Takers Scoring PRO or DIS

PCT_GRD5_MILES_EOG_ELA_PRO_DIS = Percent of Grade 5 Milestones EOG ELA Test Takers Scoring PRO or DIS

CT_GRD8_MILES_EOG_ELA_PRO_DIS = Count of Grade 8 Milestones EOG ELA Test Takers Scoring PRO or DIS

PCT_GRD8_MILES_EOG_ELA_PRO_DIS = Percent of Grade 8 Milestones EOG ELA Test Takers Scoring PRO or DIS

CT_GRD3_MILES_EOG_MATH_PRO_DIS = Count of Grade 3 Milestones EOG Math Test Takers Scoring PRO or DIS

PCT_GRD3_MILES_EOG_MATH_PRO_DIS = Percent of Grade 3 Milestones EOG Math Test Takers Scoring PRO or DIS

CT_GRD5_MILES_EOG_MATH_PRO_DIS = Count of Grade 5 Milestones EOG Math Test Takers Scoring PRO or DIS

PCT_GRD5_MILES_EOG_MATH_PRO_DIS = Percent of Grade 5 Milestones EOG Math Test Takers Scoring PRO or DIS

CT_GRD8_MILES_EOG_MATH_PRO_DIS = Count of Grade 8 Milestones EOG Math Test Takers Scoring PRO or DIS

PCT_GRD8_MILES_EOG_Math_PRO_DIS = Percent of Grade 8 Milestones EOG Math Test Takers Scoring PRO or DIS

CT_MILES_EOC_ALGEBRA_PRO_or_DIS = Count of Milestones EOC Algebra Test Takers Scoring PRO or DIS

PCT_MILES_EOC_ALGEBRA_PRO_DIS = Percent of Milestones EOC Algebra Test Takers Scoring PRO or DIS

DENOM_TOT_GRD3_MILES_EOG_ELA = Denominator - Total Count of Grade 3 Milestones EOG ELA Test Takers

DENOM_TOT_GRD5_MILES_EOG_ELA = Denominator - Total Count of Grade 5 Milestones EOG ELA Test Takers

DENOM_TOT_GRD8_MILES_EOG_ELA = Denominator - Total Count of Grade 8 Milestones EOG ELA Test Takers

DENOM_TOT_GRD3_MILES_EOG_MATH = Denominator - Total Count of Grade 3 Milestones EOG Math Test Takers

DENOM_TOT_GRD5_MILES_EOG_MATH = Denominator - Total Count of Grade 5 Milestones EOG Math Test Takers

DENOM_TOT_GRD8_MILES_EOG_MATH = Denominator - Total Count of Grade 8 Milestones EOG Math Test Takers

DENOM_TOT_MILES_EOC_ALG_TAKERS = Denominator - Total Count of Milestones EOC Algebra Test Takers

last_edited_date = Last date the feature was edited by ARC

Source: Georgia Department of Education, Atlanta Regional Commission

Date: 2016

For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2012-2016, to show counts and percentages for school enrollment by education level, by census tract in the Atlanta region. The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2012-2016). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, click here.Attributes: GEOID10 = 2010 Census tract identifier (combination of Federal Information Processing Series (FIPS) codes for state, county, and census tract) County = County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county) Area_Name = 2010 Census tract name- - - - - -Total_Population = # Total Population, 2016 Total_Population_MOE_2016 = # Total population (Margin of Error), 2016- - - - - -Num_3YrsOvr_Enrolled_School = # Population 3 years and over enrolled in school, 2016 Num_3YrsOvr_Enrolled_School_MOE = # Population 3 years and over enrolled in school (Margin of Error), 2016 Num_NurserySchool_Preschool = # Enrolled in Nursery school, preschool , 2016 Num_NurserySchool_Preschool_MOE = # Enrolled in Nursery school, preschool (Margin of Error), 2016 Pct_NurserySchool_Preschool = % Enrolled in Nursery school, preschool , 2016 Pct_NurserySchool_Preschool_MOE = % Enrolled in Nursery school, preschool (Margin of Error), 2016 Num_Kindergarten = # Enrolled in Kindergarten , 2016 Num_Kindergarten_MOE = # Enrolled in Kindergarten (Margin of Error), 2016 Pct_Kindergarten = % Enrolled in Kindergarten , 2016 Pct_Kindergarten_MOE = % Enrolled in Kindergarten (Margin of Error), 2016 Num_Elem_school_grades_1_8 = # Enrolled in Elementary school (grades 1-8) , 2016 Num_Elem_school_grades_1_8_MOE = # Enrolled in Elementary school (grades 1-8) (Margin of Error), 2016 Pct_Elem_school_grades_1_8 = % Enrolled in Elementary school (grades 1-8) , 2016 Pct_Elem_school_grades_1_8_MOE = % Enrolled in Elementary school (grades 1-8) (Margin of Error), 2016 Num_High_school_grades_9_12 = # Enrolled in High school (grades 9-12) , 2016 Num_High_school_grades_9_12_MOE = # Enrolled in High school (grades 9-12) (Margin of Error), 2016 Pct_High_school_grades_9_12 = % Enrolled in High school (grades 9-12) , 2016 Pct_High_school_grades_9_12_MOE = % Enrolled in High school (grades 9-12) (Margin of Error), 2016 Num_College_or_Grad_school = # Enrolled in College or graduate school, 2016 Num_College_or_Grad_school_MOE = # Enrolled in College or graduate school (Margin of Error), 2016 Pct_College_or_Grad_school = % Enrolled in College or graduate school, 2016 Pct_College_or_Grad_school_MOE = % Enrolled in College or graduate school (Margin of Error), 2016- - - - - -Planning_Region = Planning region designation for ARC purposes AcresLand = Land area within the tract (in acres) AcresWater = Water area within the tract (in acres) AcresTotal = Total area within the tract (in acres) SqMi_Land = Land area within the tract (in square miles) SqMi_Water = Water area within the tract (in square miles) SqMi_Total = Total area within the tract (in square miles) TRACTCE10 = Census tract Federal Information Processing Series (FIPS) code. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively. CountyName = County Name last_edited_date = Last date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2012-2016

For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.