Dickinson County

Here are the key demographics for Dickinson County, Michigan (latest U.S. Census Bureau data: 2023 Population Estimates; 2018–2022 ACS 5‑year):

• Population: ~25.5k (2023 estimate)
• Age: median ~47 years; under 18 ~20%; 65+ ~24%
• Sex: female ~50%
• Race/ethnicity:
  • White alone ~94%; Black ~0.6%; American Indian/Alaska Native ~1.1%; Asian ~0.5%; Two or more races ~3%
  • Hispanic or Latino (any race) ~2%; White alone, not Hispanic ~93%
• Households (2018–2022):
  • ~11.2k households; average household size ~2.25
  • ~63% family households; ~51% married-couple families
  • ~24% of households have children under 18
  • ~31% of households are individuals living alone (about 14% age 65+ living alone)

Note: Shares are rounded; “Hispanic or Latino” is an ethnicity and can overlap with race categories. Sources: U.S. Census Bureau, 2023 Population Estimates; 2018–2022 American Community Survey 5‑year.

• Estimated email users: 18,000–21,000 residents (roughly 70–80% of ~26,000 population), based on Michigan/US internet and email adoption rates applied locally.
• Age distribution (share using email, est.):
  • 13–17: 70–80%
  • 18–44: 95%+
  • 45–64: 90%+
  • 65+: 60–75% (lower in the oldest cohorts)
• Gender split: Approximately even, mirroring the county’s population (about half female, half male).
• Digital access trends:
  • Household broadband subscription likely around 80–85% in town centers, lower in rural townships; smartphone-only households ~10–15%.
  • Increasing fiber/cable availability in Iron Mountain–Kingsford–Norway; many outlying areas rely on DSL, fixed wireless, or satellite.
  • Public access points (libraries, schools, municipal Wi‑Fi) remain important for lower-income and rural residents.
  • Mobile email usage continues to grow, particularly among younger adults and service-sector workers.
• Local density/connectivity facts:
  • Population ~26k spread over ~760 sq mi (≈34 residents per sq mi), creating higher last‑mile costs and patchy rural coverage.
  • Most residents cluster along the US‑2/US‑141 corridor (Iron Mountain/Kingsford/Norway), where connectivity—and thus email use—is highest.

Mobile phone usage in Dickinson County, Michigan — summary (with county-vs-state highlights)

User estimates (order-of-magnitude, 2024)

• Population base: ~26,000 residents; older than Michigan overall.
• Adult mobile phone users (any mobile): ~19,000–20,000 adults (≈93–96% of adults), slightly below Michigan’s near‑universal level, reflecting a larger 65+ population.
• Adult smartphone users: ~16,500–17,700 (≈80–85% of adults), below Michigan’s ~88–90% rate; the gap is concentrated among seniors and lower‑income households.
• 5G‑capable device holders: ~11,000–13,000 users (≈65–75% of local smartphone owners), a few points below Michigan’s larger metros due to slower device replacement cycles.
• Mobile-only or mobile‑primary internet households: ~25–30% of households, above the Michigan average (≈15–20%), driven by spotty wired options outside Iron Mountain–Kingsford–Norway and growing fixed‑wireless adoption.
• Prepaid share: higher than state (county ≈25–30% vs MI ≈20–23%), consistent with income mix and cross‑carrier coverage hedging in rural areas.

Demographic patterns that shape usage (and how they differ from state)

• Age: 65+ share is well above the state; this pulls down smartphone take‑up and app‑centric use. Feature phones and voice/text‑first habits persist more than in Michigan’s metro counties.
• Income/education: Median income and BA+ attainment are lower than Michigan averages, correlating with:
  • More prepaid plans and value Android devices.
  • Slower replacement of handsets (fewer 5G phones) and lower premium‑plan uptake.
• Race/ethnicity: The county is overwhelmingly White; digital‑divide gaps are driven more by age, income, and geography than by race (unlike some state urban areas).
• Work profile: Manufacturing, healthcare, retail, forestry/outdoors lead to:
  • Higher use of rugged devices and employer‑issued basic smartphones in certain jobs.
  • Heavier reliance on coverage along highways and job sites versus dense indoor urban coverage typical downstate.

Digital infrastructure and coverage notes

• Coverage footprint:
  • Strongest, multi‑carrier coverage in Iron Mountain–Kingsford–Norway and along US‑2 and M‑95; weaker or intermittent signal in forested townships and low‑lying areas away from corridors.
  • Cross‑border spillover from Wisconsin towers (e.g., Florence/Marinette counties); roaming/partner coverage can outperform in fringe zones—more salient locally than for most Michigan counties.
• 5G mix and capacity:
  • Low‑band 5G (coverage-first) from major carriers is common in towns and highways.
  • Mid‑band 5G (capacity, e.g., C‑Band/n77 or n41) appears concentrated in the Iron Mountain urban area and selected corridor sites; rural sectors remain LTE‑heavy. This leaves a wider urban‑rural capacity gap than Michigan’s statewide average.
  • Real‑world speeds show greater variance than state metro areas: town centers can see 5G in the high tens to low hundreds Mbps; rural LTE often falls to tens of Mbps or less at peak.
• Carriers and regional dynamics:
  • Verizon and AT&T have the broadest rural footprints; T‑Mobile has improved highway/town coverage but still lags in some remote areas compared with state averages.
  • Regional influence from Cellcom/Nsight on the Wisconsin side matters more here than in most Michigan counties (plan selection and roaming behavior).
• Backhaul and siting:
  • Fiber backhaul is present along primary corridors; off‑corridor sites often depend on microwave links, which constrains rural sector capacity compared with Michigan’s metro fiber density.
  • Tower siting is valley/ridge‑sensitive; dead zones persist where terrain and trees block line‑of‑sight—more limiting than in much of the Lower Peninsula.
• Fixed wireless as a substitute:
  • 5G/LTE home internet from T‑Mobile and Verizon is available in and around population centers and is spreading outward; take‑up is meaningfully higher than the state average where cable/DSL are weak.

Behavioral/usage trends that differ from Michigan overall

• Above‑average mobile‑primary households and hotspotting for home use, especially beyond cable footprints.
• Higher share of voice/text‑centric users and lower app/video intensity among seniors; streaming and gaming are more constrained by rural capacity.
• Seasonal spikes (tourism, cabins, hunting/snowmobile seasons) create weekend/holiday congestion patterns atypical of downstate metros.
• Device turnover is slower; 5G adoption trails state urban counties by a cycle, which dampens mid‑band 5G utilization even where it exists.

Method notes and confidence

• Estimates synthesize 2020–2023 Census/ACS demographics, Pew U.S. smartphone adoption trends, FCC mobile coverage/broadband maps, and typical rural adoption discounts for the Upper Peninsula. County‑specific mobile subscription counts aren’t published; figures are modeled ranges with moderate confidence for directionality (higher/lower than state) and conservative numeric bands. For planning, validate with carrier‑reported coverage, FCC BDC filings, and on‑the‑ground drive tests in target townships.

Below is a concise, data‑informed snapshot for Dickinson County, MI. County‑level social stats aren’t directly published, so figures use 2024 U.S. averages (Pew Research Center) applied to local demographics. Population assumed ≈25,000; adults ≈20,000.

Headline user stats

• Estimated adult social media penetration: ~75–85% (≈15,000–17,000 adults)
• Teens (13–17): near‑universal use of at least one platform; ≈1,400–1,600 teens
• Gender mix of users likely mirrors population (~52% female / 48% male), with platform skews noted below

Most‑used platforms (estimated adult reach in the county; share of adults)

• YouTube: ~80–85% (≈16–17k)
• Facebook: ~65–70% (≈13–14k)
• Instagram: ~45–50% (≈9–10k)
• Pinterest: ~30–35% (≈6–7k; over‑indexes among women)
• TikTok: ~30–35% (≈6–7k; skews under 35)
• Snapchat: ~30% (≈6k; concentrated in teens/20s)
• LinkedIn: ~25–30% (≈5–6k; concentrated among professionals)
• WhatsApp: ~25–30% (≈5–6k; used mainly for messaging/family)
• X/Twitter: ~20–25% (≈4–5k; more male, news/sports oriented)
• Reddit: ~20–25% (≈4–5k; more male, hobby/tech/outdoors)

Age patterns (local implications)

• Teens (13–17): YouTube, TikTok, Snapchat dominant; Instagram strong; Facebook mainly for school/sports pages.
• 18–29: Instagram/TikTok daily; Snapchat for messaging; YouTube for creators/how‑tos.
• 30–49: Heavy Facebook (Groups/Marketplace) and YouTube; Instagram secondary; TikTok rising in early 30s.
• 50–64: Facebook + YouTube are primary; Messenger widely used.
• 65+: Facebook is the main network; YouTube for tutorials/news; lighter multi‑platform use.

Gender breakdown (modeled from national patterns)

• Women: higher share on Facebook, Instagram, Pinterest; drive local shopping, events, school content.
• Men: higher share on YouTube, Reddit, X/Twitter; strong in sports, outdoors, tools/DIY.
• Net effect: platform audiences in the county likely skew female on Facebook/Instagram/Pinterest and male on YouTube/Reddit/X.

Behavioral trends (observed in rural Upper Peninsula communities)

• Facebook Groups = community hub: local news, school sports, road/snow updates, lost‑and‑found; Marketplace is a top buy/sell channel.
• Seasonal spikes: hunting season, snowmobile/ice conditions, severe weather, summer festivals, and youth sports drive large engagement waves.
• Video first: YouTube for DIY, repairs, outdoor gear; short‑form Reels/TikTok for quick updates. Cross‑posting Reels to Facebook is common for small businesses.
• Messaging over posting among under‑30s: Snapchat and Instagram DMs dominate day‑to‑day communication.
• Trust and reach: posts from schools, municipalities, first responders, local media, and well‑known community members outperform generic brand content.
• Timing: evenings and weekends perform best; early morning check‑ins common among 50+.

Notes and sources

• Estimates apply Pew Research Center’s Social Media Use (2024) benchmarks to ACS‑style population splits for Dickinson County.
• For precise local figures, fielding a short resident survey or analyzing local page/group insights will replace these modeled estimates.