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.

File List

  Acorn_data.txt (md5: 7c7db30d39b36313243ab0042b902d11) Description
    Acorn_data.txt is a tab-delimited ASCII file. The file contains data for mean acorn production (measured as mean number of acorns counted per 30 sec) for Q. lobata and Q. douglasii for 12 California sites surveyed between 1994 and 2011 and weather data as estimated from the PRISM database. 
     Column definitions: 
     1 = Site name
     2 = Latitude (decimal degrees)
     3 = Longitude (decimal degrees)
     4 = Year
     5 = Species
     6 = Mean log-transformed count (acorns per 30 sec)
     7 = Mean maximum April temperature
     8 = Mean maximum March temperature
     9 = March rainfall
     10 = April rainfall
     11 = March rainfall the prior year (year x - 1)
     12 = April rainfall the prior year (year x - 1)
     13 = Mean maximum summer temperature (May through August)
     14 = Mean maximum summer temperature the prior year (year x - 1)
     15 = Winter rainfall (Nov. of year x - 1 through Feb. of year x)
     16 = Winter rainfall the prior year (Nov. of year x - 2 through Feb. of year x - 1)
     17 = Mean minimum winter temperature
     18 = Mean maximum autumn temperature (Sept. through Nov. of year x - 1)
     19 = Mean maximum March temperature the prior year (year x - 1) 
     20 = Mean maximum April temperature the prior year (year x - 1)
     21 = 'Combined environmental variable' for Q. lobata (see text)
     22 = 'Combined environmental variable' for Q. douglasii (see text)

The Maxent modeling algorithm was used to build the species distribution model at 270 m spatial resolution using species occurrence points and environmental layers as predictors (Phillips et al. 2006). Species occurrence points were primarily obtained from CNDDB (California Natural Diversity Database) and other CDFW sources, GBIF (Global Biodiversity Information Facility), PRBO (Point Blue Conservation Science) and Arctos museum databases. Vegetation, distance to water, elevation, and bioclimatic variables (Franklin et al. 2013) were used as predictor variables. The models were run at 270 m spatial resolution with five replications using cross-validation as a method of sample evaluation. Cross-validation involved the partitioning of the sample data into n subsets, fitting the models to n-1subsets, and testing the model on the one subset not used in fitting the model. Initial model runs showed that our models converged around 2,000 iterations and for this reason we ran all models with 2,500 maximum iterations. Maxent was implemented in R using the ‘dismo''package (Hijmans et al. 2011). Model evaluation was carried out using the ‘PresenceAbsence''package in R (Freeman and Moisen 2008). We used AUC as a metric to evaluate model performance. The package also computes threshold values using several accuracy metrics to translate predicted probability maps into binary suitable and unsuitable habitats. We selected the MeanProb, a threshold set based on the mean predicted probability of species occurrences. The output from Maxent are grid datasets in a multiband ‘tif''format with one band for each replication. We averaged the five replicated maps and created a mean grid for each species. The grid was then symbolized to represent low (threshold-50), medium (50-75) and high (75-100) habitat suitability, with pixel values that are below the threshold excluded. Models were reviewed by CDFW species experts; please review the use limitations.For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

269 Global export shipment records of Acorn Nut with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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.

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From in-depth market analysis to customer preferences and behaviors, Acorn Online's data repository is a treasure trove of valuable insights. Their collection of data spans across various industries, including consumer goods, healthcare, finance, and technology, providing businesses with a comprehensive understanding of market trends and consumer attitudes. With Acorn Online, businesses can gain valuable insights to stay ahead of the competition and drive growth.

1369 Global import shipment records of Acorn Nut with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

10 Global export shipment records of Acorn Card with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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).

This dataset provides information about the number of properties, residents, and average property values for Acorn cross streets in Irvine, CA.

Non-traditional data signals from social media and employment platforms for ACFN stock analysis

This dataset provides information about the number of properties, residents, and average property values for Acorn Court cross streets in Veneta, OR.

Acorn International Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

The seasonal match between folivore and leaf phenology affects the annual success of arboreal folivore populations because many folivores exploit developing leaves, which are an ephemeral resource. One strategy for folivores to exploit early-season leaves is to anticipate their emergence. The consequence of this behavior for trees is that individuals that set leaves earlier may experience greater rates of folivore damage, with potential negative fitness consequences. To test this hypothesis, we surveyed the early-season phenology, insect folivore damage, and acorn crop of a population of valley oaks (Quercus lobata) over a 3-year period. We found that trees that set leaves earlier experienced greater rates of folivore damage than trees that set leaves later in the season. In addition, we observed a lagged effect of folivore damage on acorn production, whereby trees with greater leaf damage produced fewer acorns in the subsequent year. These results indicate potential negative fitness consequences of earlier leaf phenology. Our study suggests that folivore pressure may be one factor that affects the optimal timing of leaf set in oaks.

Access Acorn export import data including profitable buyers and suppliers with details like HSN code, Price, Quantity.

Oak And Acorn Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Acorn abundance has been tracked annually since 1995 at Black Rock Forest (BRF), Cornwall, NY between September and October to coincide with peak acorn drop. From 1995 to 2010 all acorns were counted within a circular plot thrown 10 times at 15 to 20 locations throughout BRF. Beginning in 2004 individual oak trees were visited and DBH and number of acorns under the drip line of the tree were counted at the same 15 to 20 locations within BRF.

Traffic analytics, rankings, and competitive metrics for acorn.tv as of September 2025

Acorn Faire Acorn Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.