Our address datasets contain all geospatial address data of United States. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in United States

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

These data were compiled to help understand how climate change may impact dryland pinyon-juniper ecosystems in coming decades, and how resource management might be able to minimize those impacts. Objective(s) of our study were to model the demographic rates of PJ woodlands to estimate the areas that may decline in the future vs. those that will be stable. We quantified populations growth rates across broad geographic areas, and identified the relative roles of recruitment and mortality in driving potential future changes in population viability in 5 tree species that are major components of these dry forests. We used this demographic model to project pinyon-juniper population stability under future climate conditions, assess how robust these projected changes are, and to identify where on the landscape management strategies that decrease tree competition would effectively resist population decline. These data represent estimated recruitment, mortality and population growth across the distribution of five common pinyon-juniper species across the US Southwest. These data were collected by the US Forest service in their monitoring program, which is a systematic survey of forested regions across the entire US. Our data is from western US states, including AZ, CA, CO, ID, MT, NM, ND, NV, OR, SD, TX, UT, and was collected between 2000-2007, depending on state census collection times. These data were collected by the Forest Inventory and Analysis program of the USDA US Forest Service. Within each established plot, all adult trees greater than 12.7 cm (5 in.) diameter at breast height (DBH) are assigned unique tags and tracked within four, 7.32 m (24 ft.) radius subplots. All saplings <12.7 cm & > 2.54 cm (1 in.) DBH are assigned unique tags and tracked within four, 2.07 m (6.8 ft.) radius microplots within the larger adult plots. Finally, seedlings <2.54 cm DBH are counted within the same microplots as the saplings. Two censuses were conducted 10 years apart in each plot. These data can be used to inform how tree species have unique responses to changing climate conditions and how management actions, like tree density reduction, may effectively resist transformation away from pinyon-juniper woodland to other ecosystem types.

Our address datasets contain all geospatial address data of The Netherlands. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in The Netherlands

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

Local Perspective is a configurable app template that provides information based on a user defined location. A buffered distance around the user defined location is used to return features from features layers in the map. Use CasesDisplays the amenities, demographic, lifestyle, and weather information within a buffer of an address or point. This is a good choice for showing data that describes resources such as restaurants, parking lots, theaters, and museums available near an address.Provides directions to features within a radius of a user-selected point. This is a good choice when you want to allow users to find directions to a point of interest in a local area or for routing to destinations in more than one feature layer.Compares layers within a buffered distance of an address or point. The collection of layers can be scrolled through to gain an understanding of the variation between the layers within the current buffer. This is a good choice for showing data comparing availability of resources like schools, police stations, fire stations, and hospitals, or for comparing different types of crimes committed near an address.Configurable OptionsLocal Perspective can be used to show local amenities and can be configured using the following options:Choose a title, logo image, and color scheme.Enable US demographics, US lifestyles, and live weather.Enable routing directions as well as opt to store organization subscription credentials for public use of the app (users cannot see credentials).Set the default and maximum distance values for the buffered area, as well as distance units.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis application requires a feature layer to take full advantage of its capabilities. For more information, see the Layers help topic for more details.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

Our address datasets contain all geospatial address data of Poland. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in Poland

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

DRAKO is a Mobile Location Audience Targeting provider with a programmatic trading desk specialising in geolocation analytics and programmatic advertising. Through our customised approach, we offer business and consumer insights as well as addressable audiences for advertising.

Mobile Location Data can be meaningfully transformed into Audience Targeting when used in conjunction with other dataset. Our expansive POI Data allows us to segment users by visitation to major brands and retailers as well as categorizes them into syndicated segments. Beyond POI visits, our proprietary Home Location Model determines residents of geographic areas such as Designated Market Areas, Counties, or States. Relatedly, our Home Location Model also fuels our Geodemographic Census Data segments as we are able to determine residents of the smallest census units. Additionally, we also have audiences of: ticketed event and venue visitors; survey data; and retail data.

All of our Audience Targeting is 100% deterministic in that it only includes high-quality, real visits to locations as defined by a POIs satellite imagery buildings contour. We never use a radius when building an audience unless requested. We have a horizontal accuracy of 5m.

Additionally, we can always cross reference your audience targeting with our syndicated segments:

Overview of our Syndicated Audience Data Segments: - Brand/POI segments (specific named stores and locations) - Categories (behavioural segments - revealed habits) - Census demographic segments (HH income, race, religion, age, family structure, language, etc.,) - Events segments (ticketed live events, conferences, and seminars) - Resident segments (State/province, CMAs, DMAs, city, county, sub-county) - Political segments (Canadian Federal and Provincial, US Congressional Upper and Lower House, US States, City elections, etc.,) - Survey Data (Psychosocial/Demographic survey data) - Retail Data (Receipt/transaction data)

All of our syndicated segments are customizable. That means you can limit them to people within a certain geography, remove employees, include only the most frequent visitors, define your own custom lookback, or extend our audiences using our Home, Work, and Social Extensions.

In addition to our syndicated segments, we’re also able to run custom queries return to you all the Mobile Ad IDs (MAIDs) seen at in a specific location (address; latitude and longitude; or WKT84 Polygon) or in your defined geographic area of interest (political districts, DMAs, Zip Codes, etc.,)

Beyond just returning all the MAIDs seen within a geofence, we are also able to offer additional customizable advantages: - Average precision between 5 and 15 meters - CRM list activation + extension - Extend beyond Mobile Location Data (MAIDs) with our device graph - Filter by frequency of visitations - Home and Work targeting (retrieve only employees or residents of an address) - Home extensions (devices that reside in the same dwelling from your seed geofence) - Rooftop level address geofencing precision (no radius used EVER unless user specified) - Social extensions (devices in the same social circle as users in your seed geofence) - Turn analytics into addressable audiences - Work extensions (coworkers of users in your seed geofence)

Data Compliance: All of our Audience Targeting Data is fully CCPA compliant and 100% sourced from SDKs (Software Development Kits), the most reliable and consistent mobile data stream with end user consent available with only a 4-5 day delay. This means that our location and device ID data comes from partnerships with over 1,500+ mobile apps. This data comes with an associated location which is how we are able to segment using geofences.

Data Quality: In addition to partnering with trusted SDKs, DRAKO has additional screening methods to ensure that our mobile location data is consistent and reliable. This includes data harmonization and quality scoring from all of our partners in order to disregard MAIDs with a low quality score.

Worldwide, biodiversity is threatened by human-induced habitat degradation and fragmentation. Dispersal, particularly long-distance dispersal between isolated habitat fragments, is key for population connectivity and species persistence in the face of environmental change. However, we lack understanding of how habitat fragmentation and degradation itself affect the dispersal process. To identify conditions that promote or constrain connectivity, we need to reveal how habitat, demographic, and climatic conditions drive dispersal success and distance. This is challenging, however, because detecting dispersal events in wild animals, especially over long distances, is notoriously difficult. Here we address this in the Endangered purple-crowned fairy-wren, Malurus coronatus, a small cooperatively breeding songbird in which individuals can opt to delay natal dispersal, and we are able to consistently detect dispersal by colour-marked individuals, including over long distances. Thus, an a..., , , # Climate, habitat and demography predict dispersal by an endangered bird in a fragmented landscape

https://doi.org/10.5061/dryad.6t1g1jx95

Description of the data and file structure

Long-term data on purple-crowned fairy-wren movement was collected, to analyse drivers of natal dispersal likelihood and distance.

Files and variables

File: Data_Teunissen_JPE.xlsx

Description:

This file contains the data used to analyse drivers of natal dispersal likelihood and distance in purple-crowned fairy-wrens, as presented in Teunissen et al. 2025. Climate, habitat and demography predict dispersal by an endangered bird in a fragmented landscape. Journal of Applied Ecology.

The file contains two separate sheets:

• "Dispersal likelihood" -Â Data file used to analyse the effect of habitat, climate and demography on individual purple-crowned fairy-wrens' likelihood to disperse from their natal territory to a dominant breeding position....

Our address datasets contain all geospatial address data of North Macedonia. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in North Macedonia

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

Our address datasets contain all geospatial address data of Iceland. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in Iceland

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

Our address datasets contain all geospatial address data of Norway. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in Norway

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

Dwelling and population counts in elevation classes within 10Km, 5Km and 1Km of the coastline by ecozone, ecoprovince, ecoregion and ecodistrict for every fifth year starting with 2016.

Our address datasets contain all geospatial address data of Slovakia. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in Slovakia

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

Aim: To show how recent declines in populations of long-distance migrant birds are associated with recent increases in human population growth and agricultural intensification on their tropical non-breeding grounds, except for synanthropic species, where we expect the reverse.

Location: Breeding populations throughout Europe and North America spending the non-breeding season throughout Africa, and Central and South America, respectively.

Methods: We mapped 50 species of long-distance migrant birds from published tagging studies of 126 breeding populations and identified their breeding population trends from 2000-2015 from published Country or State census data. We then matched individual bird non-breeding locations, from each population, to local human population change and crop yield data. We used GLMs to predict whether bird population decline was associated with human population change or crop yield and whether this was dependent on if a species was synanthropic or not, controllin...

Dramatic local population decline brought about by anthropogenic-driven change is an increasingly common threat to biodiversity. Seabird life history traits that make them particularly vulnerable to such change, therefore understanding population connectivity and dispersal dynamics is vital for successful management. Our study used a 360 base-pair mitochondrial control region locus sequenced for 103 individuals and 18 nuclear microsatellite loci genotyped for 245 individuals to investigate population structure in the Atlantic and Pacific populations of the pelagic seabird, Leach’s storm-petrel Oceanodroma leucorhoa leucorhoa. This species is under intense predation pressure at one regionally important colony on St Kilda, Scotland, where a disparity between population decline and predation rates hints at immigration from other large colonies. AMOVA, FST, ΦST and Bayesian cluster analyses revealed no genetic structure among Atlantic colonies (Global ΦST = -0.02 P >0.05, Global FST = 0.003, P>0.05, STRUCTURE K = 1), consistent with either contemporary gene flow or strong historical association within the ocean basin. The Pacific and Atlantic populations are genetically distinct (Global ΦST = 0.32 P <0.0001, Global FST = 0.04, P <0.0001, STRUCTURE K = 2), but evidence for inter-ocean exchange was found with individual exclusion/assignment and population coalescent analyses. These findings highlight the importance of conserving multiple colonies at a number of different sites and suggest that management of this seabird may be best viewed at an oceanic scale. Moreover, our study provides an illustration of how long-distance movement may ameliorate the potentially deleterious impacts of localised environmental change, although direct measures of dispersal are still required to better understand this process.

The "Forest Proximate People" (FPP) dataset is one of the data layers contributing to the development of indicator #13, “number of forest-dependent people in extreme poverty,” of the Collaborative Partnership on Forests (CPF) Global Core Set of forest-related indicators (GCS). The FPP dataset provides an estimate of the number of people living in or within 1 kilometer of forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level.

For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022 report. Rome, FAO.

Contact points:

Maintainer: Leticia Pina

Distributor: Sarah E., Castle

Data lineage:

The FPP data are generated using Google Earth Engine. Forests are defined by the Copernicus Global Land Cover (CGLC) (Buchhorn et al. 2020) classification system’s definition of forests: tree cover ranging from 15-100%, with or without understory of shrubs and grassland, and including both open and closed forests. Any area classified as forest sized ≥ 1 ha in 2019 was included in this definition. Population density was defined by the WorldPop global population data for 2019 (WorldPop 2018). High density urban populations were excluded from the analysis. High density urban areas were defined as any contiguous area with a total population (using 2019 WorldPop data for population) of at least 50,000 people and comprised of pixels all of which met at least one of two criteria: either the pixel a) had at least 1,500 people per square km, or b) was classified as “built-up” land use by the CGLC dataset (where “built-up” was defined as land covered by buildings and other manmade structures) (Dijkstra et al. 2020). Using these datasets, any rural people living in or within 1 kilometer of forests in 2019 were classified as forest proximate people. Euclidean distance was used as the measure to create a 1-kilometer buffer zone around each forest cover pixel. The scripts for generating the forest-proximate people and the rural-urban datasets using different parameters or for different years are published and available to users. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022. Rome, FAO.

References:

Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.E., Herold, M., Fritz, S., 2020. Copernicus Global Land Service: Land Cover 100m: collection 3 epoch 2019. Globe.

Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M. and Schiavina, M., 2020. Applying the degree of urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics, p.103312.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University, 2018. Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

Online resources:

GEE asset for "Forest proximate people – 1km cutoff distance (100-m resolution)"

The data for the research project SCORE – Sub-national Context and Radical Right Support in Europe was collected in 2017. The research project focuses on analysing the causes and effects of sub-national differences in support for the radical right in Germany with the aim of improving the existing patterns. The survey evaluated opinions and attitudes on a range of topics, including Euroscepticism, right-wing populism, attitudes towards Islam, globalisation, political identification and participation. The dataset was collected as part of the German component of the transnational project Sub-National Context and Radical Right Support in Europe (SCoRE), which involves France, Germany, the Netherlands and the United Kingdom. The online survey was conducted by infratest dimap (N=25976).

Another aim of the survey is to analyse the data at a small-scale regional level. To this end, the datasets were categorised into a regional structure during data processing. Based on the respondents´ address data, geocodes (meridians and parallel arcs) were assigned to each data set using the offline software Map&Market premium. The data records were located within a regional radius defined by the Federal Statistical Office on the basis of the previously determined geocodes. For this purpose, a number of 362,000 grid cells with a size of one square kilometre were used. Grid cells in which the number of respondents was less than six were aggregated to avoid the possibility of re-identification. In accordance with the provisions of the German Data Protection Act, address data and survey data were kept separate at all times during the process and all process steps were supervised by infratest dimap´s data protection officer. In particular, data protection was ensured by mapping addresses and geocodes completely offline. The sample was adjusted to the demographic structures of the universe derived from official statistics. The current population extrapolation and the current “Mikrozensus” of the Federal Statistical Office were used as the data basis. Population distributions are generally adjusted for regional criteria such as Nielsen regions and municipality size classes (BIK10), as well as region (East/West), age groups, gender and educational attainment. Further information can be found at: https://www.score.uni-mainz.de/

Our address datasets contain all geospatial address data of Bosnia-Herzegovina. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in Bosnia-Herzegovina

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

Our address datasets contain all geospatial address data of Romania. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in Romania

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

Our address datasets contain all geospatial address data of Lithuania. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in Lithuania

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.

Our address datasets contain all geospatial address data of Bulgaria. You can use this data to send direct mail campaigns to households within a certain radius of your store, or to limit your online campaigns to viewers within a specific catchment area.

Address-Level Targeting in Bulgaria

Spotzi users can also combine our address data with consumer demographics and behavior data - such as insights into purchasing habits or disposable income - to ensure that every campaign targets their best-fit customers.