As of October 2020, the average amount of mobile data used by Apple Maps per 20 minutes was 1.83 MB, while Google maps used only 0.73 MB. Waze, which is also owned by Google, used the least amount at 0.23 MB per 20 minutes.

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

As of March 2021, Waze was the mobile GPN navigation app found to collect the largest amount of data from global iOS users, with 21 data points collected across all examined segments. Maps.me collected a total of 20 data points from its users, including five data points on contact information. Hiking and trail GPS map Gaia followed, with 13 data points, respectively.

The global delivery driver GPS app market is experiencing robust growth, driven by the burgeoning e-commerce sector and the increasing demand for efficient last-mile delivery solutions. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $45 billion by 2033. This growth is fueled by several key factors. Firstly, the rise of on-demand delivery services and the expansion of e-commerce necessitate reliable and efficient navigation and route optimization tools for delivery drivers. Secondly, the integration of advanced features like real-time traffic updates, delivery optimization algorithms, and proof-of-delivery functionalities enhances operational efficiency and customer satisfaction, driving adoption. Finally, the increasing affordability and accessibility of smartphones and robust mobile data networks have broadened the market reach. Significant market segmentation exists, with cloud-based solutions dominating due to their scalability and cost-effectiveness. However, on-premises solutions maintain a presence, especially among businesses with stringent data security requirements. The market is highly competitive, with established players like Google, Apple, and TomTom alongside a range of specialized providers catering to specific niches. North America and Europe currently hold the largest market shares, reflecting high e-commerce penetration and technological adoption rates. However, rapid growth is anticipated in Asia-Pacific regions due to increasing smartphone usage and the expansion of e-commerce in developing economies. The competitive landscape is dynamic, with both established tech giants and specialized startups vying for market share. Strategic partnerships, acquisitions, and continuous product innovation are key strategies employed by companies to gain a competitive edge. Challenges include data privacy concerns, the need for accurate and up-to-date map data, and ensuring seamless integration with existing logistics systems. Despite these challenges, the long-term outlook for the delivery driver GPS app market remains positive, with continued growth fueled by technological advancements and the ever-increasing reliance on efficient delivery networks. Future growth will likely be shaped by the integration of Artificial Intelligence (AI) for route optimization and predictive analytics, as well as the expansion into new markets and applications within the broader logistics industry.

This raster dataset represents the agricultural census data quality for cashew apple crop yields. Data quality categories include (0= missing, 0.25= county level census data, 0.5= interpolated with census data from within 2 degrees of latitude/longitude, 0.75= state level census data, 1= country level census data). Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003.

This is a GPS dataset acquired from Google.

Google tracks the user’s device location through Google Maps, which also works on Android devices, the iPhone, and the web.
It’s possible to see the Timeline from the user’s settings in the Google Maps app on Android or directly from the Google Timeline Website.
It has detailed information such as when an individual is walking, driving, and flying.
Such functionality of tracking can be enabled or disabled on demand by the user directly from the smartphone or via the website.
Google has a Take Out service where the users can download all their data or select from the Google products they use the data they want to download.
The dataset contains 120,847 instances from a period of 9 months or 253 unique days from February 2019 to October 2019 from a single user.
The dataset comprises a pair of (latitude, and longitude), and a timestamp.
All the data was delivered in a single CSV file.
As the locations of this dataset are well known by the researchers, this dataset will be used as ground truth in many mobility studies.

Please cite the following papers in order to use the datasets:

T. Andrade, B. Cancela, and J. Gama, "Discovering locations and habits from human mobility data," Annals of Telecommunications, vol. 75, no. 9, pp. 505–521, 2020.
10.1007/s12243-020-00807-x (DOI)
and
T. Andrade, B. Cancela, and J. Gama, "From mobility data to habits and common pathways," Expert Systems, vol. 37, no. 6, p. e12627, 2020.
10.1111/exsy.12627 (DOI)

This dataset was produced as part of the Land Use Opportunities: Whitiwhiti Ora research programme funded by the Our Land and Water National Science Challenge. Further information about this layer and links to download the data, can be found at the Whitiwhiti Ora Data Supermarket.

N.B. The information provided here is not sufficiently accurate for detailed farm-scale use.

Geologisk kart over Svalbard (1:250 000).

https://api.npolar.no/dataset/eafafbb7-b3df-4c71-a2df-316e80a7992e/_file/daf3eeae9d3aeb5bdf9a2b9f86ba8bab?key=8ee185b7c7f70470041e8801b3451517+Uyhjrqc9jddVIG52JAZO6t00BYN7eakD" alt="Mobilkart i felt">

Sammendrag (for English see below)

Dette geologiske kartet fra Norsk Polarinstitutt har blitt produsert med tanke på å brukes på smart-telefon, nettbrett eller PC uten nett-tilkobling, for eksempel til feltarbeid eller som et hendig oppslags-kart. Kartet består av 5 raster-filer i GIS-formatet JPEG2000 og er tilgjengelig som nedlasting fra datasenteret til Norsk Polarinstitutt

Informasjon om de geologiske enhetene er plassert som tekst-merkelapper direkte i kartbildet, i motsetning til en vanlig tegnforklaring. Ved å zoome inn på kartet finnes informasjon om geologiske enheter, vist med blå tekst (alder i parentes). I tillegg er hvert enhet (farge) merket med en tilsvarende 4-sifret kode i blå skrift.

I felten kan mobile dingser med GPS vise brukeren sin posisjon på kartet. Avhengig av skjermoppløsning er full detaljgrad i kartet synlig på ca. 1:30 000-skala, men kartet kan også vises på mye større skala for å se f.eks. regionale geologiske trekk.

Kartet kan vises på Android eller iOS-enheter med appen "Geoviewer" fra Extensis (tidligere Lizardtech). På datamaskin fungerer QGIS eller ArcMap bra for å vise kartet. Se forklaring på hvordan overføre kartet til din smart-telefon eller nettbrett lenger nede på sida.

Data

Kartet er laget ved å bruke data fra Norsk Polarinstitutt 1:250 000-skala geologiske kart for Svalbard, opprinnelig publisert i "Geoscience Atlas of Svalbard" av Dallmann (ed.) 2015. Dette kartet er generalisert fra 1:100 000-skala kart-data i hovedkartserien til Norsk Polarinstitutt, og er publisert i Geoscience Atlas of Svalbard (Dallmann 2015).

Til å produsere dette kartet er topografiske data fra S100 (topografi, vann) og S250 (kystlinje)-datasettene fra Norsk Polarinstitutt brukt. Fjellskygge er konstruert med S0 Terrengmodell med 20 meter pr. pixel oppløsning. Bre og snøflekk-områder er vist med datasettet for 2001-2010 av König mfl. (2013), som gir et mer oppdatert bilde av blotning-situasjonen nær breer og snøflekker. Områder der geologiske polygoner ikke er justert til nye blotninger er vist i brunt. Kystlinjen er i noen tilfeller endret for å tilpasses bre-fronter som ender i sjøen.

Forbehold om datakvalitet Dette er et nytt geologisk kartprodukt, og det kan forekomme feil. Spesielt tegnforklaring, som er skrevet direkte på geologiske enheter, kan være problematisk i noen områder. Vi er interessert i tilbakemelding på mulige forbedringer av kartet. Send gjerne tilbakemeldinger på e-post til Geokart@npolar.no.

Dette er et geologisk kart ment for å formidle vitenskapelige data, og er ikke egnet for navigasjon. Noen områder av Svalbard er ennå ikke kartlagt i detalj, og en del av dataene er av eldre dato, så datakvaliteten for dette kartet er varierende. Kartet kan inneholde feil i grunnlagsdata, kartpresentasjon, kartografi og tekst-beskrivelser. For en stor del er geologien kartlagt for en mindre detaljert skala enn den det er mulig å oppnå med dette kartproduktet, så geologiske trekk og enheter vil i ulik grad fremstå feilplassert ved bruk av god GPS-posisjon og detaljert zoom-nivå. Breer og spesielt bre-fronter er i konstant forandring, og selv om ganske oppdaterte data er brukt for å lage kartet, vil det være feil i en del bre-posisjoner. Vær oppmerksom på at det topografiske grunnlaget som er brukt her i mange tilfeller er av nyere dato enn det som opprinnelig var brukt under kartleggingen i felt. Dette kan også føre til feil i kartet.

Geologiske kart-data vil kontinuerlig være gjenstand for re-tolkning og endring. For en full beskrivelse av kartleggingsprogrammet ved Norsk Polarinstitutt, geologiske kart-data presentert her og referanser, se Dallmann (ed.) 2015, eller besøk npolar.no

Hvordan overføre kartet til mobilenheter

Direkte nedlasting Kartet kan nå lastes ned direkte til mobilenheten via lenker øverst. Det er 5 linker, en for hvert område. Enten lagres filene på enheten, eller du vil få et valg om å åpne fila direkte i Geoviewer. NB: Sørg for at det er nok ledig lagringsplass på mobilenheten og vær oppmerksom på fil-størrelsen (550 MB), spesielt hvis det er et betalt internett-abonnenement.

Via PC, kabel eller Dropbox:

NP_S250_Geologi_mobilkart kan brukes direkte i GIS-systemer på PC, mens for bruk på nettbrett og mobil anbefales gratis-appen Geoviewer fra Lizardtech.

Etter å ha lastet ned til PC og pakket opp ZIP-filene, kan kartene for Android-enheter eksempelvis overføres til ønsket plassering på enheten via USB-kabel. For iOS-enheter kan en bruke f.eks. nettjenesten Dropbox som kanal fra PC til enhet. Når kartene er lagret på enheten, kan en legge til de kartrutene en ønsker fra menyen i Geoviewer.

Referanser Kartdata Svalbard 1:100 000 (S100 Kartdata) (2014). Norwegian Polar Institute (Tromsø, Norway): https://data.npolar.no/dataset/645336c7-adfe-4d5a-978d-9426fe788ee3

M König, J Kohler, C Nuth (2013). Glacier Area Outlines - Svalbard. Norwegian Polar Institute https://data.npolar.no/dataset/89f430f8-862f-11e2-8036-005056ad0004

Dallmann, W.K., (ed.) (2015). Geoscience Atlas of Svalbard, Norsk Polarinstitutt Rapportserie nr. 148

Terrengmodell Svalbard (S0 Terrengmodell) (2014). Norwegian Polar Institute (Tromsø, Norway): https://data.npolar.no/dataset/dce53a47-c726-4845-85c3-a65b46fe2fea

English

Geological map of Svalbard (1:250 000).

Abstract This geological map from the Norwegian Polar Institute has been prepared to be used offline on a smartphone, tablet or computer, for example for field work or a handy reference. It consists of 5 raster-files in the JPEG2000 GIS-format, available to download from the Norwegian Polar Institute data centre data.npolar.no via https://data.npolar.no/dataset/eafafbb7-b3df-4c71-a2df-316e80a7992e/.

Information about the geological units has been placed as text labels (in blue typescript) directly on the map, as opposed to a regular legend. By zooming in, information about each geological unit on the map can be found, shown in blue text (age in parentheses). In addition, each unit is labelled with a corresponding 4-digit code also in blue typescript.

In the field, GPS-enabled devices can show the user's location on the map. Depending on screen resolution, full detail of the map (including text labels) is best viewed at ca. 1:30 000 scale, but the map can also be viewed at much larger scales to see e.g. regional geological features.

For mobile use, the app "Geoviewer" from Extensis (formerly Lizardtech) can be used. On a computer, QGIS works well to view these maps. See an explanation below on how to transfer the map to your tablet or smartphone.

Data

The map is made using data from the Norwegian Polar Institute 1:250 000-scale geological map for Svalbard, originally published in Dallmann (ed.) 2015. This geological map has been generalised from the 1:100 000-scale main map series published by the Norwegian Polar Institute, and is published in Geoscience Atlas of Svalbard (Dallmann 2015).

For the purpose of this map product, topographic data from the Norwegian Polar Institute S100 Map (topography, water) and S250 (coastline) data sets have been used. Hill shade was created using the NPI S0 Terrengmodell at 20 meters/pixel resolution. Glacier and snow patch outlines are shown using the 2001-2010 dataset of glacier area outlines for Svalbard by König et al. (2013), which gives a more up to date picture of the outcrop situation near glaciers or snow patches. Areas where geology polygons have not been re-adjusted to the new outcrops are shown in brown. The coast line-data has been adjusted in some cases to adapt to glacier fronts ending in the sea.

Disclaimer This is a new geological map product, and errors may occur. In particular the legend, which have been printed directly on the geological units, can be problematic in places. We appreciate feedback on the map that can be used to improve the map in future versions. Please email feedback to Geokart@npolar.no.

This is a geological map meant to convey scientific data, and is not suited for navigation. This map product may contain errors in base data, map presentation, cartography and text descriptions. Much of the geology was originally mapped for a less detailed scale than what is possible to obtain with this map, so geological features will to varying degrees appear out-of place when a good GPS-position and detailed zoom level is used. Glaciers and in particular glaciers fronts are dynamic features, and although using fairly up-to-date data, this map does contain errors in glacier front positions. Note that the topographic base data used here in many cases is of a newer vintage than the data originally used for geological mapping in the field. This may cause some errors in the map. Some areas of Svalbard have not yet been mapped in detail and some of the data are of older origin, so the data quality presented on this map is variable.

Geological map data will be subject to continual re-interpretation and editing. For a full description of the bedrock mapping programme at the Norwegian Polar Institute, the geological map data presented here and

Suitability is based on consideration of multiple suitability criteria, and expressed as a score from 0 (totally unsuitable) to 1 (perfectly suited with no limitations with respect to any criteria). Potential yield was estimated as a theoretical maximum (based on the published literature) weighted by suitability scores for suitability criteria directly related to productivity, and is an estimate of production when climate and land limitations are not mitigated. Date: May 2023 Owner: MPI Contact: Kumar Vetharaniam, Plant and Food Research

This replaces SPECIES_HABITAT_V4_HDNV and _HINV This data series is a set of spatial maps describing importance of suitable habitat within the current extent of native vegetation for some species. These species are threatened and their habitat is described as either dispersed or highly localised (ie remaining habitat within Native Vegetation is either greater than or less than 2000 ha respectively based on IUCN categorisation). These maps have been developed for use in the native vegetation permitted clearing regulations. Thresholds have been applied to the maps because lower likelihood areas do not receive species-specific consideration in the assessment process. Habitat importance is continuous for dispersed species, for highly localised habitats however it is equal across the map. The values stored in these data are used to calculate risk and offsets under the native vegetation permitted clearing regulations (as proposed 2013)

Note that for dispersed species the values stored in these data must be divided by 100 to calculate the habitat importance score.

Dataset Series Naming: vic-hpnv-v4-[a/p][taxa_id]_2013nov19_thm_bio_75m_vg94.tif where v4 is the version of the source models (ie.SPECIES_HABITAT_V4_HDNV) a/p animal/plant taxa_id is VBA taxa_id

This replaces SPECIES_HABITAT_V4_HDNV and _HINV This data series is a set of spatial maps describing importance of suitable habitat within the current extent of native vegetation for some species. These species are threatened and their habitat is described as either dispersed or highly localised (ie remaining habitat within Native Vegetation is either greater than or less than 2000 ha respectively based on IUCN categorisation). These maps have been developed for use in the native vegetation permitted clearing regulations. Thresholds have been applied to the maps because lower likelihood areas do not receive species-specific consideration in the assessment process. Habitat importance is continuous for dispersed species, for highly localised habitats however it is equal across the map. The values stored in these data are used to calculate risk and offsets under the native vegetation permitted clearing regulations (as proposed 2013)

Note that for dispersed species the values stored in these data must be divided by 100 to calculate the habitat importance score.

Dataset Series Naming: vic-hpnv-v4-[a/p][taxa_id]_2013nov19_thm_bio_75m_vg94.tif where v4 is the version of the source models (ie.SPECIES_HABITAT_V4_HDNV) a/p animal/plant taxa_id is VBA taxa_id

This raster dataset depicts the average fractional proportion of a gridcell for cashew apple crops that were harvested circa 2000. Croplands cover ~15 million km2 of the planet and provide the bulk of the food and fiber essential to human well-being. Most global land cover datasets from satelites group croplands into just a few categories, thereby excluding information that is critical for answering key questions ranging from biodiversity conservation to food security to biogeochemical cycling. Information about agricultural land use practices like crop selection, yield, and fertilizer use is even more limited.Here we present land use data sets created by combining national, state, and county level census statistics with a recently updated global data set of croplands on a 5 minute by 5 minute (~10km x 10 km) latitude/longitude grid. Temporal resolution: Year 2000- based of average of census data between 1997-2003.

Pearson’s correlation coefficient results between percent land cover and GPS only and WiFi enabled data.

Pearson’s correlation coefficient results between weather conditions and GPS only and WiFi enabled data.

The local search engine market is experiencing robust growth, driven by the increasing reliance on mobile devices and the expanding adoption of location-based services. The market, estimated at $50 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 12% through 2033, reaching approximately $150 billion. This expansion is fueled by several key factors: the rising number of smartphone users globally, the proliferation of location-based apps and services (including ride-sharing, food delivery, and e-commerce), and the increasing sophistication of search algorithms in providing highly localized and personalized results. Businesses are increasingly investing in local SEO strategies to enhance their online visibility and attract customers within their geographic proximity, further contributing to market growth. Segmentation within the market reflects this diverse usage, with significant contributions from individual users seeking local information and businesses employing these platforms for marketing and customer engagement. The competition among established players like Google, Yelp, and Facebook, along with emerging niche players, ensures a dynamic and innovative market landscape. However, the market also faces certain challenges. Data privacy concerns and regulations are increasingly impacting how local search engines collect and utilize user data. The evolving landscape of online advertising and the complexities of managing online reputations also pose challenges for both businesses and users. Furthermore, maintaining accuracy and consistency in local business listings across various platforms remains a significant hurdle. Despite these restraints, the long-term outlook for the local search engine market remains positive, driven by ongoing technological advancements, increasing mobile penetration, and the continued evolution of consumer behavior. The strategic expansion into emerging markets, especially in Asia Pacific and Africa, presents substantial opportunities for growth. The ongoing development and refinement of location-based services and improved user experiences will be crucial to shaping the future of this dynamic sector.

Family Tracking App Market Outlook

The global family tracking app market size is projected to reach USD 11.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.5% from 2023. The market size in 2023 stands at USD 5.4 billion. The substantial growth of this market is driven by increasing concerns for family safety and security, coupled with the rising penetration of smartphones and internet connectivity. The integration of advanced technologies such as GPS, AI, and IoT in family tracking apps is also a significant growth factor.

One of the primary growth factors in the family tracking app market is the rising awareness and need for personal and family security. With increasing crime rates and the need for real-time location tracking of family members, especially children and elderly people, the demand for family tracking apps has surged. These applications provide peace of mind to family members by enabling constant monitoring and quick response capabilities in case of emergencies. Moreover, the increasing adoption of smartphones globally has further facilitated the widespread use of family tracking apps.

Advancements in technology, particularly in GPS and AI, have significantly enhanced the capabilities and reliability of family tracking apps. Modern apps offer features such as geofencing, real-time location sharing, and emergency alerts, which are highly valued by users. The continuous evolution of these technologies ensures that family tracking apps can provide more accurate and timely information, thereby improving user experience and trust. Additionally, the integration of these apps with other smart devices such as wearables and smart home systems further amplifies their utility and appeal.

The COVID-19 pandemic has also played a crucial role in accelerating the adoption of family tracking apps. With lockdowns and social distancing measures in place, families have turned to these apps to stay connected and ensure the safety of their loved ones. The pandemic has highlighted the importance of staying informed about the whereabouts and well-being of family members, leading to an increased demand for reliable tracking solutions. This trend is expected to continue post-pandemic, as the habit of using these apps becomes ingrained in daily life.

Regionally, North America holds a significant share of the family tracking app market due to high smartphone penetration, technological advancements, and heightened awareness about personal safety. Europe follows closely, driven by similar factors along with stringent regulations regarding child safety. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to rapid urbanization, increasing internet penetration, and growing awareness about family safety. In contrast, the markets in Latin America, the Middle East, and Africa are smaller but are showing promising growth due to increasing adoption of smartphones and awareness about tracking technologies.

Platform Analysis

The platform segment of the family tracking app market is divided into iOS, Android, and Windows. Among these, the Android platform holds the largest market share due to the widespread use of Android devices globally. Android's open-source nature allows for greater customization and integration with various tracking features, making it a popular choice for developers and users alike. Additionally, the affordability of Android smartphones has made these apps more accessible to a broader audience, contributing to the segment's dominance.

iOS, while not as dominant as Android in terms of market share, still holds a significant portion of the market, particularly in regions like North America and Europe where Apple devices are highly popular. The iOS platform is known for its strong security features and seamless integration with other Apple products, which appeals to users who prioritize data privacy and a cohesive digital ecosystem. The consistent performance and regular updates of iOS also enhance the reliability of family tracking apps on this platform.

The Windows platform, although the smallest in terms of market share, caters to a niche segment of users. Windows-based family tracking apps are often used in enterprise settings where Windows devices are prevalent. Despite its limited share, the Windows platform benefits from the strong security protocols and enterprise-level features that are inherent to the Windows operating system. This makes it a viable option for businesses looking to ensure the safety of their employees.

This shapefile contains boundaries representing the Town of Apple Valley City Council Districts for the purpose of establishing election divisions within a district. This dataset should only be used for the purpose of establishing election divisions within a district. It will be removed once the redistricting process has concluded.To download:1. Click the Download button above.2. A side panel will appear showing download options. Under Shapefile, click the Download button.3. When the download completes, browse to the location of the downloaded .zip, copy it to the location where you manage your redistricting files, then right-click to extract the contents. You will then be able to use the file in GIS software.If, rather than downloading the data, you wish the reference online versions of these datasets directly to ensure you are always using the most up-to-date data, please contact the County of San Bernardino Innovation and Technology Departments at 909-884-4884 or by emailing OpenData@isd.sbcounty.gov for informations and instructions for doing so.

As of December 2024, Google’s Chrome accounted for 65.82 percent of the global web browser market share. Firefox and Internet Explorer have experienced massively reduced market share in recent years as Chrome’s influence has expanded, and new competitors have entered the market. How are web browsers changing? In the UK, smartphones have overtaken traditional desktop computers as the most popular way to access the internet. Tech giants such as Samsung and Apple have used their smartphone hardware as a platform to encourage the use of their mobile web browsers. Each iPhone comes pre-loaded with Apple’s Safari set as its default internet browser, and consumers have become increasingly comfortable with the platform without having to seek it out themselves specifically. Google’s role in the web Throughout the years, Google has become increasingly vertically integrated at many levels of the tech industry. Already controlling the world’s most used search engine, the company also owns the world’s most popular web browser (Google Chrome), the world’s most popular email service (Gmail), the world’s most popular GPS mapping service (Google Maps), and has recently branched out into hardware itself, with its line of smartphones and wearable devices.