Abstract copyright UK Data Service and data collection copyright owner.

The Acorn geodemographic classification is a long-running classification developed by CACI Limited. Acorn operates by merging geography with demographics and details about consumer characteristics and behaviours. Supported by advanced AI methods, comprehensive input data, and detailed product literature, Acorn provides precise information and enables an in-depth understanding of the different types of consumers in every part of the country.

The current classification groups the entire United Kingdom population into 7 categories, 22 groups and 65 types. The data is available at unit postcode level. Further information may be found on the CACI ACORN microsite.

Use of the data requires approval from the data owner or their nominee and is restricted to those based at a Higher Education or Further Education institution. Please see the Data Access section for further information.

For the second edition (October 2024) data and documentation files for 2024 have been added to the study.

A survey conducted by the United Kingdom's Department for Digital, Culture, Media and Sport from May 2023 to March 2024 analyzed the share of adults who visited a museum or gallery in England in the previous 12 months. While ** percent of the respondents from the "Rising Prosperity" Acorn demographic type reported visiting a museum or gallery, just ** percent of "Urban Adversity" respondents mentioned the same.

This statistic presents the proportion of adults who engaged with the arts in England in 2018/19, by ACORN classification. Arts engagement refers to attending and/or participating in the arts sector, such as visiting an art gallery or participating in a theatre performance. Of the "Affluent Achievers" group, **** percent had engaged in at least one art event or activity in the last year, compared to **** percent classified as "Financially Stretched".

Understanding Society (the UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Verian Group (formerly Kantar Public) and NatCen. It builds on and incorporates, the British Household Panel Survey (BHPS), which began in 1991.

This dataset contains Wellbeing Acorn geodemographic segmentation codes (group and type) for each household in every wave of Understanding Society, together with a household identification number (hidp) allowing it to be linked to the main Understanding Society data files. The dataset is produced by matching the Wellbeing Acorn segmentation against every Understanding Society household at the postcode level.

The Wellbeing Acorn segmentation system itself is developed and maintained by CACI Ltd and is designed by analysing demographic data, social factors, health and wellbeing characteristics in order to provide an understanding of the population’s wellbeing across the country. Group is the higher layer containing 5 segments providing a snapshot of the population from the least healthy to the healthiest. The more granular level is Type, containing 25 segments, to provide more detailed insights about the population to better understand their demographic, lifestyle and health characteristics. For details on the Acorn segmentation structure and how is it is produced please refer to the documentation and the Caci website.

These data have more restrictive access conditions than those available under the standard End User Licence (see 'Access data' tab for more information).

NOTE: Please choose the download option to access the profiles. A collection of ward wellbeing profiles created using the resident/customer classification tool Acorn. These profiles are created using Cheshire East addresses from the National Address Gazetteer. The postcode is based on the predominant classification of the households in a particular postcode. For more information, including information about the sources of data, please visit the data providers website.

Percentage of pupils achieving level 5 or above in Key Stage 3 tests by ACORN category of pupil residency

Source: Department for Education and Skills (DfES)

Publisher: Department for Children Schools and Families (DCSF)

Geographies: Local Authority District (LAD), County/Unitary Authority, Government Office Region (GOR), National

Geographic coverage: England

Time coverage: 2006

Type of data: Administrative data

Notes: Acorn category based on pupil postcode

The main British Household Panel Survey (BHPS) is conducted by the ESRC UK Longitudinal Studies Centre (ULSC), together with the Institute for Social and Economic Research (ISER) at the University of Essex. In addition to conducting the BHPS and disseminating it to the research community, ISER undertakes a programme of research based on panel data, using the BHPS and other national panels to monitor and measure social change.

The main objective of the BHPS is to further understanding of social and economic change at the individual and household level in the UK, and to identify, model and forecast such changes and their causes and consequences in relation to a range of socio-economic variables. It is conducted as a longitudinal study, where each adult member (aged 16 years and over) of a sampled household is interviewed annually. If individuals leave their original household, all adult members of their new households are interviewed. Children are also interviewed. For full details of the BHPS methodology, sampling, changes over time, and a complete set of documentation, see the main BHPS study, held at the UK Data Archive under SN 5151.

Understanding Society:
From Wave 19, the BHPS has been subsumed into a new longitudinal study called Understanding Society, or the United Kingdom Household Longitudinal Study (UKHLS), conducted by ISER. The BHPS Wave 19 formed part of Understanding Society Wave 2 (January 2010 - March 2011). The BHPS fieldwork period therefore moved from September-April to January-March. This means that the gap between interviews 18 and 19 for the BHPS sample ranges between 16 and 30 months rather than the standard 12 months. From Wave 2, the BHPS sample has been a permanent part of the larger study and interviews are conducted annually again. BHPS sample members have an identifier within the Understanding Society datasets, allowing BHPS users to match BHPS Wave 1-18 data to Understanding Society. The main Understanding Society study, held under SN 6614 now includes harmonised BHPS data in addition to the main Understanding Society files. Further information is available on the Understanding Society web site.


This dataset contains Acorn geodemographic classification codes for each wave of the BHPS and a household identification serial number for file matching to the main BHPS data. This dataset is subject to restrictive access conditions, different to those for the main BHPS: see Access section below.

The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

Percentage of responses in the range 0-6 for 'Life Satisfaction' by LSOA in the First ONS Annual Experimental Subjective Wellbeing survey, April 2011 - March 2012

The Department for Communities and Local Government (DCLG) has estimated the expected wellbeing of residents at Lower-layer Super Output Area (LSOA) level. The purpose is to illustrate the likely degree of variation between neighbourhoods.

These are modelled estimates for local areas based on national findings from the ONS Annual Population Survey 2011-2012. They are not the actual survey responses of people living in those areas [1]. As such, DCLG encourage local areas to test these expected findings against their own local knowledge and data.

DCLG used CACI’s ACORN geo-demographic segmentation to estimate the likely wellbeing characteristics of each neighbourhood. Analysis of the APS provided a national profile of wellbeing by ACORN Type, with estimates of average subjective wellbeing and low subjective wellbeing for each of the 56 Types. The national profile was then applied to localities, to reflect their composition according to ACORN Type [2].

The method presumes the national profile of wellbeing for the ACORN types is broadly the same in each local authority. For all of the subjective wellbeing measures, DCLG tested this assumption broadly held across the nine regions. As a result, DCLG made a minimal number of adjustments to the profiles for life satisfaction, worthwhile, and happy yesterday, and determined that the method was not robust for modelling anxiety [3].

Feedback on the neighbourhood estimates and requests for further details of the methodology can sent to wellbeing@communities.gsi.gov.uk.

In October, DCLG will be producing wellbeing profiles to enable users to apply the same methodology using geo-demographic classifications: Experian’s MOSAIC and ONS’s Output Area Classification (OAC).

[1] This is because sample sizes from the APS do not permit reliable estimates of subjective wellbeing below the 90 unitary authorities and counties reported in the First ONS Annual Experimental Subjective Well-being Results.

[2] ACORN is a segmentation based on shared characteristics of people’s life-stage, income, profession and housing, as well as characteristics of places including whether they are urban, suburban or rural. Each respondent on the APS had been classified into one ACORN Type, based on the full postcode in which they live – approximately 16 addresses.) ACORN provided estimates of the population in each ACORN Type in each LSOA and local authority district.

[3] These adjustments were made only where there was reliable evidence (based on samples of more than 100 respondents) from APS that the national wellbeing ACORN profile was substantially different from the regional one, and where the implications for neighbourhood maps would be highly geographically clustered.

Percentage of responses in the range 0-6 for 'Happy Yesterday' by LSOA in the First ONS Annual Experimental Subjective Wellbeing survey, April 2011 - March 2012

The Department for Communities and Local Government (DCLG) has estimated the expected wellbeing of residents at Lower-layer Super Output Area (LSOA) level. The purpose is to illustrate the likely degree of variation between neighbourhoods.

These are modelled estimates for local areas based on national findings from the ONS Annual Population Survey 2011-2012. They are not the actual survey responses of people living in those areas [1]. As such, DCLG encourage local areas to test these expected findings against their own local knowledge and data.

DCLG used CACI’s ACORN geo-demographic segmentation to estimate the likely wellbeing characteristics of each neighbourhood. Analysis of the APS provided a national profile of wellbeing by ACORN Type, with estimates of average subjective wellbeing and low subjective wellbeing for each of the 56 Types. The national profile was then applied to localities, to reflect their composition according to ACORN Type [2].

The method presumes the national profile of wellbeing for the ACORN types is broadly the same in each local authority. For all of the subjective wellbeing measures, DCLG tested this assumption broadly held across the nine regions. As a result, DCLG made a minimal number of adjustments to the profiles for life satisfaction, worthwhile, and happy yesterday, and determined that the method was not robust for modelling anxiety [3].

Feedback on the neighbourhood estimates and requests for further details of the methodology can sent to wellbeing@communities.gsi.gov.uk.

In October, DCLG will be producing wellbeing profiles to enable users to apply the same methodology using geo-demographic classifications: Experian’s MOSAIC and ONS’s Output Area Classification (OAC).

[1] This is because sample sizes from the APS do not permit reliable estimates of subjective wellbeing below the 90 unitary authorities and counties reported in the First ONS Annual Experimental Subjective Well-being Results.

[2] ACORN is a segmentation based on shared characteristics of people’s life-stage, income, profession and housing, as well as characteristics of places including whether they are urban, suburban or rural. Each respondent on the APS had been classified into one ACORN Type, based on the full postcode in which they live – approximately 16 addresses.) ACORN provided estimates of the population in each ACORN Type in each LSOA and local authority district.

[3] These adjustments were made only where there was reliable evidence (based on samples of more than 100 respondents) from APS that the national wellbeing ACORN profile was substantially different from the regional one, and where the implications for neighbourhood maps would be highly geographically clustered.

This dataset tracks annual total students amount from 2005 to 2023 for Acorn High School

Masting is a population-level reproductive strategy, where individuals synchronize large but intermittent seed production. Despite the high degree of synchrony at the population level, there can be considerable variation in reproduction among individuals (intraspecific variation). Here, we use 18 years of acorn production data from individual chestnut oak and black oak from control and thinned stands, to understand what factors influence individual differences in reproductive effort and variability. We included a variety of tree-level measurements, environmental characteristics, and measurements from tree cores to determine if certain characteristics were associated variations in reproduction. We considered both mean annual acorn production per m2 crown and interannual variation in acorn production (CV) as response variables. We also classified individuals as super producers (i.e., those that consistently produce more acorns than others), good, fair and poor producers (i.e., those that consistently produce less or have a higher number of failure years). In chestnut oak, 14% of the individuals were classified as super producers and contributed 34% of the total acorns, while poor producers made up 35% of the trees and contributed only 16% to total acorn production. In black oak, super producers (14% of the individuals) contributed 31% of total acorns and poor producers (24% of the individuals) contributed only 9% of the acorns. Diameter at breast height (DBH) was the most consistent variable for explaining intraspecific variation in reproductive effort and variability (i.e., larger individuals had higher mean acorn production for both chestnut oak and black oak, and lower CV for black oak). Other variables that influenced reproduction and variation included elevation and clay content for chestnut oak, and slope for black oak. We found no significant effect from the thinning treatment on acorn production. Our results illustrate how tree-level and environmental characteristics can distinguish acorn production groups, which can be used to inform management and selective harvesting decisions.

We examined spatial patterns and spatial autocorrelation (synchrony) of annual acorn production in three species of oaks (genus Quercus) over a 288 km transect in central coastal California. Over small (within-site) distances of < 4 km, synchrony of acorn production between individual trees was significant but varied through time and, for coast live oaks Q. agrifolia, differed at two sites 135 km apart. On a larger geographic scale, valley Q. lobata and blue Q. douglasii oaks exhibited significant synchrony in most distance categories between trees and sites up to 135 km apart and, in the case of coast live oaks, up to the maximum extent of the transect. Spatial patterns over this geographic scale also differed among species, with valley and blue oaks, but not coast live oaks, exhibiting distinct declines in synchrony of acorn production with distance. Interspecific synchrony in acorn production was generally lower than that within species but still significant over the entireextent of the survey. Spatial synchrony between sites was to some extent related to the same environmental variables previously found to correlate with annual acorn production within a site, suggesting that the environmental factors determining acorn production locally also influence spatial patterns over larger geographic areas. These results demonstrate that mast-fruiting in oaks occurs not only on a widespread geographic scale but also across species. They also confirm that synchrony over large geographic areas and complex spatial patterns varying in time can occur in systems where dispersal does not occur and thus environmental variability (the Moran effect) alone is likely to be driving spatial dynamics.

Key Stage 1 and 2 National Curriculum assessments, GCSE and equivalent achievements and post-16 attainment of young people in England by ACORN category

Source: Department for Education and Skills (DfES)

Publisher: Department for Children Schools and Families (DCSF)

Geographies: Local Authority District (LAD), County/Unitary Authority, Government Office Region (GOR), National

Geographic coverage: England

Time coverage: 2004 to 2005

Type of data: Administrative data

In species with long-term social relationships, the ability to recognize individuals after extended separation, and the ability to discriminate between former social affiliates that have died and those that have left the group but may return, are likely to be beneficial. Few studies, however, have investigated whether animals can make these discriminations. We presented acorn woodpeckers (Melanerpes formicivorus), a group-living, cooperatively breeding bird, with playbacks of current group members, former group members still living nearby, former group members that had died or left the study area, and familiar non-group members. Subjects responded more quickly to the calls of non-group members than to the calls of current group members or former group members still living in the study area but did not discriminate between non-group members and former group members that had died or disappeared. This suggests that acorn woodpeckers can vocally recognize both current group members and former group members that have dispersed to nearby groups, and that they either forget former group members that no longer live in the vicinity, or classify them differently from former group members that still live nearby. This study suggests an important role for vocal recognition in maintaining valuable relationships with social affiliates post-dispersal.

Methods MATERIAL AND METHODS

Study site and population monitoring

All data were collected on wild acorn woodpeckers at Hastings Natural History Reservation in central coastal California, USA (36.387ºN, 121.551ºW). This population has been the subject of a long-term study since 1968 (MacRoberts and MacRoberts 1976; Koenig 1981b), and >95% of the individuals are color-banded. Most individuals are banded in the nest at 21 days of age, and unbanded adults immigrating into the population are captured and banded whenever possible. As of 2019, there are approximately 50 social groups within the study area, and each group is censused approximately every 8–10 weeks using spotting scopes to re-sight color banded individuals.

Experiment 1: vocal discrimination of current, former, and non-group members

We conducted Experiment 1 14 Apr–19 Jun 2017 and 2 May–14 Jun 2018. Subjects were 7 females and 7 males from 8 social groups, and all but one were of breeder status. We presented each subject with 3 different playback stimuli on different days: (1) a call of a current group member; (2) a call of a former group member that had died, left the group, or remained on the natal territory after the subject had dispersed 1.1–6.4 years prior to the experiment (median = 2.8 years); and (3) a call of an unrelated individual from a nearby territory that had never lived in the same group as the subject. The distance between the territory centroids of the subject and the non-group member caller was 40–862 m (median=167 m), and in 9 of 14 non-group member trials the caller and subject shared a territorial boundary. As acorn woodpeckers make frequent forays to other territories with a mean foray distance of 500–600 m, subjects were likely familiar with all of the non-group member callers (Barve et al. 2020).

The order of the presentation was balanced according to a Latin square design (Table 1). The 3 stimuli played to a given subject were always recorded from callers of a single sex, such that each subject received either 3 male or 3 female calls. The sex of the subject matched the sex of the callers in 7 of 14 cases. Successive playbacks to the same group or to groups < 250 m from each other were spaced by 6.0 ± 4.8 days on average (minimum 3 days to same group, 2 days to groups closer than 250 m).

Testing the difference in response to callers living in the study area (hereafter “nearby”) vs. callers that had died or left the study area (hereafter “absent”) was not an a priori goal of Experiment 1. However, because of the difficulty of obtaining playback-quality recordings from known individuals, we used the call of an individual that was no longer observed on the study area at the time of the experiment as the former group member stimulus for two female and four male subjects (Table 1). None of the former group members that were classified as “absent” had been observed on the study site for at least a year prior to the experiment and none were seen at least a year post-experiment (as of Sep 2019). For two of the males that received a former group member stimulus from an absent caller, the non-group member stimulus was also from an absent caller (not seen for 7 months prior to the experiment). Among the subjects that received the call of a nearby former group member, the distance between the territory centroids of the subject and the caller was 121–1587 m (median = 228 m), and in 5 of 8 nearby former group member trials, the caller and subject shared a territorial boundary

Experiment 2: vocal discrimination of nearby and absent former group members

We conducted Experiment 2 6 Apr–12 Jul 2019, using 5 female and 6 male subjects from 8 different groups (Table 2). Six individuals were used as subjects in both Experiment 1 and Experiment 2, and among the 11 subjects used in Experiment 2, all but 2 were members of a social group that was exposed to playbacks in Experiment 1. Experiment 2 was designed as a follow-up to Experiment 1 to investigate whether acorn woodpeckers could vocally discriminate between nearby and absent former group members. We presented each subject with the call of a former group member living on a neighboring territory within the study area (nearby), the call of a former group member that had not been observed in the study area for 1.1–7.3 years before the experiment (median absence =3.0 years), and the call of an unrelated individual from a nearby territory that had never lived in the same group as the subject.

The interterritorial distance of subjects and nearby former group members was 102–257 m (median = 132 m), and all nearby former group members shared a territorial boundary with the subject. The interterritorial distance of subjects and non-group members was 102–734 m (median = 257 m), and 5 of 11 non-group members shared a territorial boundary with the subject. Thus, as in Experiment 1, subjects were likely familiar with all nearby former group member and non-group member callers. As of Sep 2019, none of the absent former group members had been observed on the study area since their last sighting 1.1–7.3 years before the experiment.

We ensured that the amount of time since the subject and caller last lived together did not statistically differ between nearby former group member and absent former group member playback stimuli (Paired t-test, t10=-1.2, P=0.25). As in Experiment 1, order of presentation was balanced according to a Latin square design (Table 2), and the 3 playback stimuli presented to a given subject were recorded from 3 callers of a single sex, which matched the sex of the subject in 6 of 11 cases. Successive playbacks to the same group or to groups <250 m apart were separated by 6.6 ± 6.5 days (minimum 2 days).

Playback stimuli

In both experiments, the calls used as playback stimuli were waka calls, an individually distinctive, affiliative call typically produced when members of the same group approach one another after a short period of separation (MacRoberts and MacRoberts 1976; Yao 2008). All playback stimuli were recorded at Hastings Reservation 19 Mar 2015–26 May 2017 using a Sennheiser ME67 or ME62 microphone (Wedemark, Germany) and a Marantz PMD661 (Kanagawa, Japan), Fostex FR-2 (Akishima City, Tokyo, Japan), or Roland R26 (Hamamatsu, Shizuoka, Japan) digital recorder (48 kHz, 16 or 24 bits). Prior to constructing the playback stimuli, the calls were high-pass filtered (200 Hz cut-off, 6 dB roll off) and normalized to -3 dB in Audacity® 2.1.1, and any calls originally recorded at 24 bits were converted to 16 bits.

In Experiment 1, the playback stimuli consisted of 60 sec of background noise with a 10-sec fade-in, followed by a single waka call, followed by 30 sec of background noise, followed by the same waka call, followed by a final 10 sec of background noise with a fade-out applied to all 10 sec. Repeating the call increased the likelihood that the subjects would respond to the playback, and the 30-sec interval between calls followed previously published protocols (Yao 2008; Pardo et al. 2018). While natural waka calls are most commonly produced singly, they are sometimes repeated at an interval close to 30 sec (M. Pardo, unpublished data). The playback stimuli for Experiment 2 were constructed in the same way except that the initial period of background noise only lasted 30 sec. We made this change to reduce the chance that the subject would fly away before the call began.

Playback protocol

Playback trials for both experiments followed a similar protocol to Pardo et al. (2018). In brief, we placed a Yamaha PDX 11 loudspeaker (Hamamatsu, Shizuoka, Japan) characterized at 100.1 ± 1.3 dB re 20 µPa at 1 m in a tree 1–1.5 m off the ground and 40 m away from a tree near the center of the group’s territory (“center tree”). This volume was at the upper end of the range of natural waka calls produced by a captive adult male acorn woodpecker (Pardo et al. 2018). The speaker was always placed in the same location during successive trials to a given group. Once the subject was located in the center tree, an observer began filming the subject using either a Canon PowerShot SX510 digital camera (Ota City, Tokyo, Japan) or a Sony Handycam DCR-SX45 Camcorder (Minato, Tokyo, Japan), and immediately played the appropriate playback file.

Measuring response to playback

Based on video and audio recordings of each playback trial, we measured the following aspects of the focal bird’s response: latency to the first “reaction” (defined as vocalizing, flying up

This dataset tracks annual distribution of students across grade levels in Acorn High School

This dataset tracks annual total classroom teachers amount from 2005 to 2023 for Acorn High School

This dataset tracks annual overall school rank from 2011 to 2022 for Acorn High School

Historical Dataset of Acorn High School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2005-2023),Total Classroom Teachers Trends Over Years (2005-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2005-2023),American Indian Student Percentage Comparison Over Years (2007-2023),Asian Student Percentage Comparison Over Years (2006-2022),Hispanic Student Percentage Comparison Over Years (2005-2023),Black Student Percentage Comparison Over Years (2011-2022),White Student Percentage Comparison Over Years (2005-2023),Two or More Races Student Percentage Comparison Over Years (2014-2023),Diversity Score Comparison Over Years (2007-2023),Free Lunch Eligibility Comparison Over Years (2005-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2005-2023),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2011-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2011-2022),Graduation Rate Comparison Over Years (2012-2022)

This dataset tracks annual diversity score from 2007 to 2023 for Acorn High School vs. Arkansas and Ouachita River School District