Status of POTA in India
#pota #field-ops #analysis #data #maps #challenges #python #india #vuland
Reading time: 28 minutesThis post is best read on a large screen!
As always, here is the TL;DR version:
- 191 parks listed, 50 parks have been activated a total of 104 times
- Starting with 2 activations in 2021, we seem to have plateaued now
- The massive opportunity of 141 unactivated parks (of the listed parks)
- Click-bait: Only 481 active HF operators in India who upload their logs!
- We only have 19 activators! General and restricted are on-par
If you have been following the excellent data-driven analysis by Frank Howell (K4FMH) on U.S. POTA activity1, you might wonder: what does the distribution of Parks on the Air (POTA) look like in VU land? In this post, we will walk through how to fetch the data, analyse it, and generate the necessary maps to understand the POTA landscape in India. If you compare this with the parks in the US or EU, the numbers look minuscule, but I would hope, that the uptick in VU POTA operations continues2.
Parks listed on POTA
How many parks do we have listed on the POTA site? Where are they? How many of these are activated?
There are 191 parks listed on the POTA site, of which 50 parks have been activated a total of 104 times by 19 operators3.
Figure 1: Interactive Map of POTA Sites in India
Click to view/hide Map Data Table
This table breaks down the geographical spread of our POTA entities alongside their lifetime activations in India. Unsurprisingly, states with active communities like West Bengal, Karnataka, and Madhya Pradesh are currently leading the charge. Given my recent wet-weather deployment at , it’s great to see Karnataka steadily climbing through.
| State / Region | Total Parks | Activated Parks | Total Activations |
|---|---|---|---|
| West Bengal | 26 | 15 | 32 |
| Karnataka | 15 | 10 | 28 |
| Madhya Pradesh | 13 | 13 | 18 |
| Tamil Nadu | 6 | 2 | 11 |
| Maharashtra | 17 | 3 | 6 |
| Haryana | 5 | 3 | 4 |
| Kerala | 9 | 1 | 2 |
| Rajasthan | 5 | 1 | 1 |
| Goa | 2 | 1 | 1 |
| Sikkim | 1 | 1 | 1 |
| Arunachal Pradesh | 24 | 0 | 0 |
| Andaman and Nicobar | 9 | 0 | 0 |
| Himachal Pradesh | 8 | 0 | 0 |
| Ladakh | 8 | 0 | 0 |
| Assam | 7 | 0 | 0 |
| Uttarakhand | 6 | 0 | 0 |
| Gujarat | 4 | 0 | 0 |
| Telangana | 4 | 0 | 0 |
| Andhra Pradesh | 3 | 0 | 0 |
| Chandigarh | 3 | 0 | 0 |
| Jammu and Kashmir | 3 | 0 | 0 |
| Manipur | 2 | 0 | 0 |
| Meghalaya | 2 | 0 | 0 |
| Odisha | 2 | 0 | 0 |
| Tripura | 2 | 0 | 0 |
| Bihar | 1 | 0 | 0 |
| Jharkhand | 1 | 0 | 0 |
| Mizoram | 1 | 0 | 0 |
| Nagaland | 1 | 0 | 0 |
| Uttar Pradesh | 1 | 0 | 0 |
Has the state of POTA changed over a period of time in India?
Is POTA catching on in VU land? The short answer is maybe not!
This could also be a result of unfamiliarity with POTA, not actively uploading logs on the POTA site, or just not listing the park!
Growth of POTA in India
I plotted a line graph to track our cumulative completed activations from the early days of 2021 up to 2026. The rate of change is slow, showing a low adoption of portable operations with VU operators.
Figure 2: Historical Timeline of POTA Activations
Click to view/hide Timeline Data Table
| Year | Activations in Year | Cumulative Total |
|---|---|---|
| 2021 | 2 | 2 |
| 2022 | 28 | 30 |
| 2023 | 17 | 47 |
| 2024 | 30 | 77 |
| 2025 | 25 | 102 |
| 2026 | 21 | 123 |
This table gives the hard numbers year-by-year, we have steadily climbed to a running total of 123 activations by 2026. Though there is a cumulative increase, if you look at the activations year-on-year, the last three years have shown an interesting trend, we had 25 activations in 2025 and 30 activations in 2024, while 2026 is currently on pace to be the most active year yet for Indian POTA. With the “low” monsoon, holiday season, and winter travel coming up, I expect the activations to double4. While one could say these are too small numbers to analyse and worry about, they are a clear sign that “catalysing” will definitely help.
Are all these parks activated?
The funny portion however is that 141 parks have not yet been activated, now is that a result of an initial set of “seeder” parks that were added or is that because of the nature of POTA in India? However, that is a lot of first-activation opportunities waiting for operators in India if they are willing to brave it out in the field, and especially in the conditions at parks.
Figure 3: Bar Chart detailing the 'Long Tail' of unactivated and lightly-activated parks
Click to view/hide Tier Breakdown Table
| Activation Tier | Number of Parks |
|---|---|
| Unactivated | 141 |
| 1 Activation | 30 |
| 2 to 5 Activations | 16 |
| 6 to 10 Activations | 4 |
| 11+ Activations | 0 |
Drawing inspiration from K4FMH’s excellent spatial analysis of U.S. parks, this curve plots the cumulative percentage of lifetime activations against individual park ranks. The inequality is staggeringāa very small fraction of popular parks accounts for a massive share of the overall activation count. The detailed table provides a complete ranking of all 191 parks, showing exactly which ones (like Mudumalai and Bandipur) are doing the heavy lifting, accounting for 18 themselves. If the top 4 parks are put together, we already have 30 of the 104 activations. If each one had an equal share, the line would be flat and straight from zero percent, rank 1 to 100 percent, rank 191 (number of POTA parks)5.
Figure 4: Lorenz Curve showing the inequality of park activations
Click to view/hide Ranked Parks Data Table
| Rank | Reference | Park Name | Activations | Cumulative % |
|---|---|---|---|---|
| 1 | IN-0085 | Mudumalai National Park | 10 | 9.62% |
| 2 | IN-0041 | Bandipur National Park | 8 | 17.31% |
| 3 | IN-0042 | Bannerghatta National Park | 6 | 23.08% |
| 4 | IN-0126 | Rabindra Sarovar Ecological Site | 6 | 28.85% |
| 5 | IN-0067 | Tadoba National Park | 4 | 32.69% |
| 6 | IN-0128 | Bethuadahari Wildlife Sanctuary Wildlife Reserve | 4 | 36.54% |
| 7 | IN-0061 | Satpura National Park | 4 | 40.38% |
| 8 | IN-0140 | Cauvery Wildlife Sanctuary Wildlife Reserve | 4 | 44.23% |
| 9 | IN-0101 | Jaldapara National Park | 3 | 47.12% |
| 10 | IN-0155 | Mayurjharna Elephant Reserve Sanctuary | 3 | 50.00% |
| 11 | IN-0176 | Mangwa Forest (Mangwa Hill) Ecological Site | 3 | 52.88% |
| 12 | IN-0044 | Nagarhole National Park | 3 | 55.77% |
| 13 | IN-0059 | Pench National Park | 2 | 57.69% |
| 14 | IN-0169 | Satpura National Park National Park | 2 | 59.62% |
| 15 | IN-0136 | MOHANPUR FOREST Recreation Park | 2 | 61.54% |
| 16 | IN-0137 | JAWAHAR KUNJ Recreation Park | 2 | 63.46% |
| 17 | IN-0185 | Wayanad Wildlife Sanctuary | 2 | 65.38% |
| 18 | IN-0182 | Damdama Lake Biodiversity Nature Park | 2 | 67.31% |
| 19 | IN-0103 | Singalila National Park | 2 | 69.23% |
| 20 | IN-0139 | Nugu Wildlife Sanctuary Wildlife Reserve | 2 | 71.15% |
| 21 | IN-0100 | Gorumara National Park | 1 | 72.12% |
| 22 | IN-0055 | Kuno National Park | 1 | 73.08% |
| 23 | IN-0058 | Panna National Park | 1 | 74.04% |
| 24 | IN-0043 | Kudremukh National Park | 1 | 75.00% |
| 25 | IN-0054 | Kanha National Park | 1 | 75.96% |
| 26 | IN-0062 | Van Vihar National Park | 1 | 76.92% |
| 27 | IN-0060 | Sanjay National Park | 1 | 77.88% |
| 28 | IN-0056 | Madhav National Park | 1 | 78.85% |
| 29 | IN-0057 | Mandala Plant Fossil National Park | 1 | 79.81% |
| 30 | IN-0053 | Omkareshwar National Park | 1 | 80.77% |
| 31 | IN-0052 | Bandhavgarh National Park | 1 | 81.73% |
| 32 | IN-0189 | Brahmagiri Wildlife Reserve | 1 | 82.69% |
| 33 | IN-0170 | Ralamandal Wildlife Sanctuary | 1 | 83.65% |
| 34 | IN-0181 | Leopard Trail Aravalli Hills State Trail | 1 | 84.62% |
| 35 | IN-0184 | Leisure Valley Park Natural Area | 1 | 85.58% |
| 36 | IN-0144 | Tipeshwar Wildlife Sanctuary Wildlife Reserve | 1 | 86.54% |
| 37 | IN-0113 | EMPRESS Botanical Gardens | 1 | 87.50% |
| 38 | IN-0107 | Sabujdeep Provincial Recreation Area | 1 | 88.46% |
| 39 | IN-0127 | Ballabhpur Wildlife Sanctuary Wildlife Reserve | 1 | 89.42% |
| 40 | IN-0076 | Desert National Park | 1 | 90.38% |
| 41 | IN-0081 | Kangchendzonga National Park | 1 | 91.35% |
| 42 | IN-0158 | Chapramari Wildlife Sanctuary Wildlife Reserve | 1 | 92.31% |
| 43 | IN-0157 | Sikia Jhora Bird Sanctuary | 1 | 93.27% |
| 44 | IN-0145 | Mahadeshwara wildlife sanctury and tiger reserve Wildlife Reserve | 1 | 94.23% |
| 45 | IN-0159 | Parade Ground Park | 1 | 95.19% |
| 46 | IN-0142 | Jayamangali Blackbuck Reserve Conservation Reserve | 1 | 96.15% |
| 47 | IN-0135 | MOHANPUR FOREST Natural Area | 1 | 97.12% |
| 48 | IN-0138 | Biligiri Ranganatha Swamy Temple (BRT) Wildlife Reserve | 1 | 98.08% |
| 49 | IN-0154 | Mangalam R.F Nature Park | 1 | 99.04% |
| 50 | IN-0183 | Mollem National Park | 1 | 100.00% |
| 51 | IN-0016 | Kaziranga National Park | 0 | 100.00% |
| 52 | IN-0004 | Middle Button Island National Park | 0 | 100.00% |
| 53 | IN-0001 | Campbell Bay National Park | 0 | 100.00% |
| 54 | IN-0002 | Galathea National Park | 0 | 100.00% |
| 55 | IN-0003 | Mahatma Gandhi Marine National Park | 0 | 100.00% |
| 56 | IN-0008 | Saddle Peak National Park | 0 | 100.00% |
| 57 | IN-0009 | South Button Island National Park | 0 | 100.00% |
| 58 | IN-0010 | Papikonda National Park | 0 | 100.00% |
| 59 | IN-0011 | Rajiv Gandhi (Rameshwaram) National Park | 0 | 100.00% |
| 60 | IN-0012 | Sri Venkateswara National Park | 0 | 100.00% |
| 61 | IN-0005 | Mount Harriet National Park | 0 | 100.00% |
| 62 | IN-0006 | North Button National Park | 0 | 100.00% |
| 63 | IN-0007 | Rani Jhansi Marine National Park | 0 | 100.00% |
| 64 | IN-0063 | Chandoli National Park | 0 | 100.00% |
| 65 | IN-0050 | Silent Valley National Park | 0 | 100.00% |
| 66 | IN-0049 | Periyar National Park | 0 | 100.00% |
| 67 | IN-0048 | Pambadum Shola National Park | 0 | 100.00% |
| 68 | IN-0051 | Hemis National Park | 0 | 100.00% |
| 69 | IN-0033 | Khirganga National Park | 0 | 100.00% |
| 70 | IN-0034 | Pin Valley National Park | 0 | 100.00% |
| 71 | IN-0035 | Simbalbara National Park | 0 | 100.00% |
| 72 | IN-0039 | Betla National Park | 0 | 100.00% |
| 73 | IN-0038 | Salim Ali National Park | 0 | 100.00% |
| 74 | IN-0037 | Kishtwar National Park | 0 | 100.00% |
| 75 | IN-0036 | Dachigam National Park | 0 | 100.00% |
| 76 | IN-0040 | Anshi National Park | 0 | 100.00% |
| 77 | IN-0045 | Anamudi Shola National Park | 0 | 100.00% |
| 78 | IN-0046 | Eravikulam National Park | 0 | 100.00% |
| 79 | IN-0047 | Mathikettan Shola National Park | 0 | 100.00% |
| 80 | IN-0032 | Inderkilla National Park | 0 | 100.00% |
| 81 | IN-0017 | Manas National Park | 0 | 100.00% |
| 82 | IN-0018 | Nameri National Park | 0 | 100.00% |
| 83 | IN-0019 | Rajiv Gandhi Orang National Park | 0 | 100.00% |
| 84 | IN-0020 | Valmiki National Park | 0 | 100.00% |
| 85 | IN-0021 | Guru Ghasidas (Sanjay) National Park | 0 | 100.00% |
| 86 | IN-0022 | Indravati (Kutru) National Park | 0 | 100.00% |
| 87 | IN-0023 | Kanger Vally National Park | 0 | 100.00% |
| 88 | IN-0024 | Mollem National Park | 0 | 100.00% |
| 89 | IN-0025 | Vansda National Park | 0 | 100.00% |
| 90 | IN-0026 | Blackbuck National Park | 0 | 100.00% |
| 91 | IN-0027 | Gir National Park | 0 | 100.00% |
| 92 | IN-0028 | Marine (Gulf Of Kutch) National Park | 0 | 100.00% |
| 93 | IN-0029 | Kalesar National Park | 0 | 100.00% |
| 94 | IN-0030 | Sultanpur National Park | 0 | 100.00% |
| 95 | IN-0031 | Great National Park | 0 | 100.00% |
| 96 | IN-0015 | Dibru-Saikhowa National Park | 0 | 100.00% |
| 97 | IN-0014 | Namdapha National Park | 0 | 100.00% |
| 98 | IN-0013 | Mouling National Park | 0 | 100.00% |
| 99 | IN-0092 | Dudhwa National Park | 0 | 100.00% |
| 100 | IN-0091 | Clouded Leopard National Park | 0 | 100.00% |
| 101 | IN-0090 | Bison ( Rajbari) National Park | 0 | 100.00% |
| 102 | IN-0089 | Mrugavani National Park | 0 | 100.00% |
| 103 | IN-0099 | Buxa Reserve | 0 | 100.00% |
| 104 | IN-0095 | Jim Corbett National Park | 0 | 100.00% |
| 105 | IN-0094 | Govind Pashu Vihar National Park | 0 | 100.00% |
| 106 | IN-0093 | Gangotri National Park | 0 | 100.00% |
| 107 | IN-0071 | Nokrek National Park | 0 | 100.00% |
| 108 | IN-0068 | Keibul Lamjao National Park | 0 | 100.00% |
| 109 | IN-0069 | Sirohi National Park | 0 | 100.00% |
| 110 | IN-0065 | Navegaon National Park | 0 | 100.00% |
| 111 | IN-0064 | Gugamal National Park | 0 | 100.00% |
| 112 | IN-0079 | Ranthambore National Park | 0 | 100.00% |
| 113 | IN-0078 | Mukundra Hill National Park | 0 | 100.00% |
| 114 | IN-0082 | Guindy National Park | 0 | 100.00% |
| 115 | IN-0080 | Sariska Reserve | 0 | 100.00% |
| 116 | IN-0083 | Gulf Of Munnar Marine National Park | 0 | 100.00% |
| 117 | IN-0084 | Indra Gandhi Sanctuary National Park | 0 | 100.00% |
| 118 | IN-0086 | Mukurthi National Park | 0 | 100.00% |
| 119 | IN-0087 | Kasu Brahmananda Reddy National Park | 0 | 100.00% |
| 120 | IN-0088 | Mahavir Harina Vanasthali National Park | 0 | 100.00% |
| 121 | IN-0072 | Murlen National Park | 0 | 100.00% |
| 122 | IN-0074 | Bhitarkanika National Park | 0 | 100.00% |
| 123 | IN-0073 | Ntangki National Park | 0 | 100.00% |
| 124 | IN-0075 | Simlipal National Park | 0 | 100.00% |
| 125 | IN-0077 | Keoladeo National Park | 0 | 100.00% |
| 126 | IN-0070 | Balphakram National Park | 0 | 100.00% |
| 127 | IN-0066 | Sanjay Gandhi National Park | 0 | 100.00% |
| 128 | IN-0124 | Daying Ering Wildlife Sanctuary 5(1)(g) Reserve | 0 | 100.00% |
| 129 | IN-0125 | MEHAO WILD LIFE SANCTUARY Biosphere Reserve | 0 | 100.00% |
| 130 | IN-0122 | Pakke Tiger Reserve 5(1)(g) Reserve | 0 | 100.00% |
| 131 | IN-0123 | TALLE VALLEY WILD LIFE SANCTUARY Protected Area | 0 | 100.00% |
| 132 | IN-0121 | KAMLANG WILDLIFE SANCTUARY Biosphere Reserve | 0 | 100.00% |
| 133 | IN-0120 | DIBANG WILD LIFE SANCTUARY Wildlife Reserve | 0 | 100.00% |
| 134 | IN-0118 | SESSA ORCHID SANCTUARY Biosphere Reserve | 0 | 100.00% |
| 135 | IN-0119 | EAGLE NEST WILD LIFE SANCTUARY 5(1)(g) Reserve | 0 | 100.00% |
| 136 | IN-0115 | BOPDEV HILL TREKKING POINT State Trail | 0 | 100.00% |
| 137 | IN-0114 | RAJIV GANDHI ZOOLOGICAL PARK State Park | 0 | 100.00% |
| 138 | IN-0117 | KOYNA UNESCO Biosphere Reserve | 0 | 100.00% |
| 139 | IN-0116 | KAS PLATEAU Forest Reserve | 0 | 100.00% |
| 140 | IN-0112 | VETAL HILL Forest Reserve | 0 | 100.00% |
| 141 | IN-0109 | Gool Poonawala Garden Recreation Park | 0 | 100.00% |
| 142 | IN-0110 | VASUNDHARA VAN 5(1)(g) Reserve | 0 | 100.00% |
| 143 | IN-0111 | ANAND VAN Reserve | 0 | 100.00% |
| 144 | IN-0096 | Nanda Devi National Park | 0 | 100.00% |
| 145 | IN-0097 | Rajaji National Park | 0 | 100.00% |
| 146 | IN-0098 | Valley Of Flowers National Park | 0 | 100.00% |
| 147 | IN-0108 | Taljai Forest Wildlife Reserve | 0 | 100.00% |
| 148 | IN-0106 | BALLABHPUR WILDLIFE SANCTUARY Wildlife Reserve | 0 | 100.00% |
| 149 | IN-0105 | PILIKULA BIOLOGICAL PARL Nature Park | 0 | 100.00% |
| 150 | IN-0104 | Sundarbans National Park | 0 | 100.00% |
| 151 | IN-0102 | Neora Valley National Park | 0 | 100.00% |
| 152 | IN-0143 | RICE RESEARCH STATION Scientific Reserve | 0 | 100.00% |
| 153 | IN-0141 | SHETTIHALLI WILDLIFE SANCTUARY Wildlife Reserve | 0 | 100.00% |
| 154 | IN-0130 | INDRA GANDHI PARK Recreation Park | 0 | 100.00% |
| 155 | IN-0129 | ITANAGAR ZOOLOGICAL PARK Recreation Park | 0 | 100.00% |
| 156 | IN-0131 | POLO PARK Botanical Gardens | 0 | 100.00% |
| 157 | IN-0132 | ITA - FORT Historical Reserve | 0 | 100.00% |
| 158 | IN-0133 | GEKER SINYING Wetland Reserve | 0 | 100.00% |
| 159 | IN-0134 | ENERGY PARK Recreation Park | 0 | 100.00% |
| 160 | IN-0156 | Maidan (Kolkata) Ecological Area | 0 | 100.00% |
| 161 | IN-0152 | Eaglenest Wildlife Sanctuary Wildlife Area | 0 | 100.00% |
| 162 | IN-0153 | Itanagar Wildlife Sanctuary Wildlife Area | 0 | 100.00% |
| 163 | IN-0147 | Pakke Tiger Reserve Wildlife Area | 0 | 100.00% |
| 164 | IN-0148 | Daying Ering wildlife sanctuary Wildlife Area | 0 | 100.00% |
| 165 | IN-0149 | Mehao Wildlife Sanctuary Wildlife Area | 0 | 100.00% |
| 166 | IN-0150 | Kamlang Wildlife Sanctuary Wildlife Area | 0 | 100.00% |
| 167 | IN-0151 | Talley Valley Wildlife Sanctuary Wildlife Area | 0 | 100.00% |
| 168 | IN-0146 | Dibang Wildlife Sanctuary Wildlife Area | 0 | 100.00% |
| 169 | IN-0166 | Amchang Wildlife Sanctuary | 0 | 100.00% |
| 170 | IN-0167 | Pobitora Wildlife Sanctuary | 0 | 100.00% |
| 171 | IN-0160 | Changthang Cold Desert Wildlife Sanctuary Wildlife Reserve | 0 | 100.00% |
| 172 | IN-0164 | Pangong Tso Aquatic Reserve | 0 | 100.00% |
| 173 | IN-0161 | Karakoram (Nubra Shyok) Wildlife Sanctuary Wildlife Reserve | 0 | 100.00% |
| 174 | IN-0162 | Tso Kar Basin Wildlife Sanctuary Wildlife Reserve | 0 | 100.00% |
| 175 | IN-0163 | Gya-Miru Wildlife Sanctuary Wildlife Reserve | 0 | 100.00% |
| 176 | IN-0175 | KHOLTA ECO PARK Landscape Park | 0 | 100.00% |
| 177 | IN-0174 | RAYDAK FOREST (RAIDAK) National Reserve | 0 | 100.00% |
| 178 | IN-0173 | PORO ECO PARK Area of Outstanding Natural Beauty | 0 | 100.00% |
| 179 | IN-0172 | Idukki Wildlife Sanctuary Wildlife Reserve | 0 | 100.00% |
| 180 | IN-0168 | Lonar Lake Wildlife Reserve | 0 | 100.00% |
| 181 | IN-0165 | Chilapata Forest Conservation Reserve | 0 | 100.00% |
| 182 | IN-0180 | Ficus Garden Botanical Gardens | 0 | 100.00% |
| 183 | IN-0179 | Bhimgad Wildlife Sanctuary Wildlife Area | 0 | 100.00% |
| 184 | IN-0177 | Konni Forest Regional Reserve | 0 | 100.00% |
| 185 | IN-0178 | Ghum Biodiversity Conservation Park Biosphere Reserve | 0 | 100.00% |
| 186 | IN-0187 | Hemis National Park | 0 | 100.00% |
| 187 | IN-0186 | Kabini Wildlife Reserve | 0 | 100.00% |
| 188 | IN-0188 | Tso Kar Lake Wildlife Sanctuary | 0 | 100.00% |
| 189 | IN-0190 | Manali Wildlife Sanctuary | 0 | 100.00% |
| 190 | IN-0191 | Great Himalayan National Park | 0 | 100.00% |
| 191 | IN-0192 | Pin Valley National Park | 0 | 100.00% |
Why is POTA activity so low in India?
The first question we will need to answer is how many “active HAMs” do we have in the country? Ideally, you’d have something like the Wireless Planning And Coordination (WPC) Wing of the Ministry of Communications, Govt. of India publishing at least the number of licensed HAMs in the country. But as far as I know, that isn’t the case. Another heuristic we could use the number of registrants/participants in HAM fests across India. I’m told across Hamfest India, Lamakaan Annual Radio Convention and ARCON, Hamfest is the largest one. The latest version HFI 2026, is at Manguluru, and according to their registration numbers we have roughly 5906 licensed HAMs registered for the event.
But how many of those are actively getting on the air? To find out, we have to look for public digital footprints. Ideally, we would just query QRZ.com, eQSL, or ClubLog, as these are incredibly popular logging platforms. However, these platforms either require paid premium subscriptions for API access, or they employ strict anti-bot protections (like Cloudflare) that prevent automated country-wide analysis. Scraping them directly violates their Terms of Service. Because of this, we must rely on completely open and public data sources. For the purposes of this post, I have built a census using a dual-methodology to capture two different types of active operators:
- Intentional Log Uploaders (3-Year Baseline) These are operators who have actively uploaded logs or participated in global events over the last three years (2024ā2026). This includes:
- Logbook of The World (LoTW) and Parks on the Air (POTA) databases.
- A comprehensive sweep of public contest submissions, including: CQ WPX (SSB/CW/RTTY), CQ WW (SSB/CW/RTTY), CQ 160 (CW/SSB), IARU HF Championship, and a massive suite of ARRL contests (DX CW/SSB, RTTY Roundup, 160M, Sweepstakes CW/SSB, Int. Digital, and 10M). I might have missed out a few folks from other contests, so please do let me know7.
- Active RF Emitters (Recent Snapshots) Not every active HAM cares about contests or uploading logs to LoTW; some just ragchew or test antennas. To capture these operators, I queried automated spotting networks8.
- Reverse Beacon Network (RBN): Downloaded one day’s archive to capture active CW and RTTY operators.
- PSKReporter: A targeted 24-hour snapshot to capture operators actively transmitting on FT8/FT4.
- WSPRnet: The current month’s archive to find operators actively transmitting weak signals for propagation testing.
| Data Source / Metric | Total Discovered | Extracted Unique |
|---|---|---|
| Logbook of The World (LoTW) | 323 | +323 |
| Parks on the Air (POTA) | 49 | +25 |
| CQ WPX SSB | 98 | +34 |
| CQ WPX CW | 32 | +3 |
| CQ WW SSB | 69 | +14 |
| CQ WW CW | 30 | +2 |
| CQ WW RTTY | 5 | +0 |
| CQ WPX RTTY | 10 | +0 |
| CQ 160 CW | 3 | +0 |
| CQ 160 SSB | 0 | +0 |
| IARU HF Champ | 70 | +21 |
| ARRL DX CW | 11 | +0 |
| ARRL DX SSB | 10 | +0 |
| ARRL RTTY Roundup | 7 | +1 |
| ARRL 160M | 0 | +0 |
| ARRL Sweepstakes CW | 0 | +0 |
| ARRL Sweepstakes SSB | 0 | +0 |
| ARRL Int. Digital | 2 | +0 |
| ARRL 10M | 39 | +5 |
| Reverse Beacon Network (24h) | 69 | +48 |
| PSKReporter (24h) | 12 | +4 |
| WSPRnet (Recent Archive) | 5 | +1 |
| Total De-duplicated Active VU Population | - | 481 |
As you can see, 481 is a small number for a country the size of India. However, it is important to contextualize this number: it represents digitally verifiable, internationally visible HF operators. It does not account for the purely local operators who rely entirely on VHF/UHF repeaters, rag-chewers, those who exclusively log on QRZ.com, or traditional HAMs who still maintain entirely paper logs. However, 481 active operators is a clear sign that the baseline is small, and we have a challenge on our hands. The fact that only 19 of these 481 operators have ventured out to activate a POTA park highlights a massive opportunity for growth in Indian portable operations!
Who is activating these parks?
When looking at the full group of 19 operators, the community delivered 123 total activations (104 of which were successful) across a footprint of 72 unique parks (50 of which were successfully activated)9. However, if you remove VU2UCR from the equation, the remaining 18 operators account for only 82 total activations (63 successful) and 57 unique parks. This means VU2UCR alone is responsible for exactly 33% of all field outings and nearly 40% of the entire dataset’s successful activations.
Activators
I put together a twin-axis butterfly chart to map operator behaviour. It plots their geographic diversity (the number of unique parks visited on the left axis) against their overall activation effort (total outings on the right axis), to denote their activation rate10. It also reveals a massive skew in the data: VU2UCR alone is responsible for an incredible 33% of all field outings and nearly 40% of the entire dataset’s successful activations. Because of this skewed volume of activations by VU2UCR, pulling him out corrects a significant skew in your averages: the mean successful activations per operator drops sharply from 5.5 down to 3.5, while the median (the most “typical” operator) remains much more grounded, shifting only slightly from 3 down to 2.5. VU2JDC has activated 13 unique parks and next up is VU3YDA on this list with 9 parks.
Figure 5: Twin-axis butterfly chart mapping operator exploration and effort.
Click to view/hide Activator Statistics Table
| Callsign | Unique Parks Activated | Total Activations | Successful (>=10 QSOs) | Failed (<10 QSOs) | Success Rate |
|---|---|---|---|---|---|
| VU2UCR | 15 | 41 | 41 | 0 | 100.0% |
| VU2JDC | 13 | 15 | 14 | 1 | 93.3% |
| VU3YDA | 9 | 14 | 11 | 3 | 78.6% |
| VU3XRY | 6 | 12 | 7 | 5 | 58.3% |
| VU3OAC | 4 | 4 | 4 | 0 | 100.0% |
| VU2UUU | 3 | 5 | 4 | 1 | 80.0% |
| VU3IBL | 3 | 6 | 4 | 2 | 66.7% |
| VU3NZZ | 3 | 3 | 3 | 0 | 100.0% |
| VU3KOX | 3 | 3 | 2 | 1 | 66.7% |
| VU3GLJ | 2 | 4 | 3 | 1 | 75.0% |
| VU3NZG | 2 | 3 | 3 | 0 | 100.0% |
| VU3UBP | 2 | 2 | 2 | 0 | 100.0% |
| VU2MYS | 1 | 2 | 2 | 0 | 100.0% |
| VU22DX | 1 | 2 | 1 | 1 | 50.0% |
| VU22PJ | 1 | 2 | 1 | 1 | 50.0% |
| VU2GIN | 1 | 1 | 1 | 0 | 100.0% |
| VU2HRF | 1 | 1 | 1 | 0 | 100.0% |
| VU2ZRF | 1 | 1 | 0 | 1 | 0.0% |
| VU3IYI | 1 | 2 | 0 | 2 | 0.0% |
What is the split between General and Restricted?
As a VU3 operator myself, this was a fun one to dig into11! Before jumping in, it is important to note that our sample size is currently quite smallāonly 9 General (VU2) and 10 Restricted (VU3) operators. While this means a single new highly active operator could easily shift the overall trends, the current data tells a compelling story. The data tells us that despite the high average, a typical general grade operator is not very active (median of 1.0, skewed mean of 7.2). The bottom 25% of the operators completed less than one activation, whereas if a general grade operator activates 4 parks, they move to the top 25%. Interestingly, restricted grade operators seem to be more active (median of 3.0 and mean of 3.9)! The bottom 25% activated 2 or fewer parks, whereas the top 25% activated 4. While the sample size is too small to draw permanent conclusions and could change rapidly in a couple of years12, the current numbers translate to the Restricted Grade (VU3) operators actually having a much stronger and more consistent baseline of activity.
Figure 6: Baseline operational footprint of General vs. Restricted grade license holders.
Click to view/hide License Class Demographics
| License Class | Active Operators | Median Activations | Maximum Activations | Total Activations (Class) |
|---|---|---|---|---|
| VU2 (General Grade) | 9 | 1 | 41 | 65 |
| VU3 (Restricted Grade) | 10 | 3 | 11 | 39 |
While the General class appears to vastly outperform the Restricted class on paper, these tables tell a different story once you adjust for the extremes. When you remove the extreme top outlier (VU2UCR), the Restricted class (VU3) operators actually maintain a higher baseline of average activity, a stronger median, and a higher mean successful activation rate than General class operators! It seems us Restricted operators are definitely pulling our weight out in the field. What would be interesting to check is how many of them are doing so with their QRP setups!
| Metric | General - all | General - without VU2UCR | Restricted |
|---|---|---|---|
| Active Operators | 9 | 8 | 10 |
| Total Attempts | 70 | 29 | 53 |
| Successful (>=10 QSOs) | 65 | 24 | 39 |
| Mean Successful | 7.2 | 3.0 | 3.9 |
| Median Successful | 1.0 | 1.0 | 3.0 |
Where do they go to activate?
I would have ideally liked to show all activators, but given the lack of “public” data for this, I have only plotted those who have listed their data on HamQTH13.
Figure 7: Geographic footprint and operational tracking radii of portable park activations.
Who is hunting Indian parks?
The map below is fairly instructive, we can clearly see that the typical hunter profile seems to be the usual suspects who are able to receive HF easily from India. Please note that for simplicity, I have only listed the top fifteen hunters and the number of unique parks in the first image, the pie chart has the full data for the hunter split between groups, and the full list of hunters is available below the charts.
Figure 8: Hunters of Indian POTA sites.
Figure 9: Map of IN park hunters.
Click to view/hide Master Hunter Leaderboard Table
| Rank | Callsign | ISO | Classification | Unique Parks Hunted | Total Confirmed Contacts |
|---|---|---|---|---|---|
| 1 | VU3TBU | IND | Domestic (VU) | 10 | 26 |
| 2 | VU3JXF | IND | Domestic (VU) | 5 | 12 |
| 3 | 4S6RYD | LKA | International (DX) | 5 | 5 |
| 4 | VU3GWN | IND | Domestic (VU) | 4 | 8 |
| 5 | VU3FBT | IND | Domestic (VU) | 4 | 8 |
| 6 | SP9RXP | POL | International (DX) | 4 | 5 |
| 7 | IK4IDF | ITA | International (DX) | 4 | 4 |
| 8 | VU3IZV | IND | Domestic (VU) | 3 | 8 |
| 9 | VU27MJ | IND | Domestic (VU) | 3 | 7 |
| 10 | S21PL | BGD | International (DX) | 3 | 4 |
| 11 | JF7RJM | JPN | International (DX) | 2 | 6 |
| 12 | VU3KOX | IND | Domestic (VU) | 2 | 6 |
| 13 | VU3HZW | IND | Domestic (VU) | 2 | 5 |
| 14 | VU2JFA | IND | Domestic (VU) | 2 | 4 |
| 15 | 9M8HAZ | MYS | International (DX) | 2 | 4 |
| 16 | VU3FWK | IND | Domestic (VU) | 2 | 4 |
| 17 | VU2DSI | IND | Domestic (VU) | 2 | 3 |
| 18 | VU3BXI | IND | Domestic (VU) | 2 | 3 |
| 19 | EV1R | BLR | International (DX) | 2 | 3 |
| 20 | VU2MYS | IND | Domestic (VU) | 2 | 3 |
| 21 | VU3IBL | IND | Domestic (VU) | 2 | 3 |
| 22 | VU3YDA | IND | Domestic (VU) | 2 | 3 |
| 23 | VU22PJ | IND | Domestic (VU) | 2 | 2 |
| 24 | VU2FFW | IND | Domestic (VU) | 2 | 2 |
| 25 | VU2UCR | IND | Domestic (VU) | 2 | 2 |
| 26 | EA3BS | ESP | International (DX) | 2 | 2 |
| 27 | PD8MD | NLD | International (DX) | 2 | 2 |
| 28 | HF5A | POL | International (DX) | 2 | 2 |
| 29 | VU2MNX | IND | Domestic (VU) | 2 | 2 |
| 30 | HS0ZPS | THA | International (DX) | 2 | 2 |
| 31 | IT9RZR | ITA | International (DX) | 1 | 4 |
| 32 | VU2VV | IND | Domestic (VU) | 1 | 3 |
| 33 | VU2GRM | IND | Domestic (VU) | 1 | 2 |
| 34 | EA4T | ESP | International (DX) | 1 | 2 |
| 35 | G5LSI | GBR | International (DX) | 1 | 2 |
| 36 | VU2JFB | IND | Domestic (VU) | 1 | 2 |
| 37 | VU2JFC | IND | Domestic (VU) | 1 | 2 |
| 38 | VU3JXB | IND | Domestic (VU) | 1 | 2 |
| 39 | VU3HIG | IND | Domestic (VU) | 1 | 2 |
| 40 | VU3YJB | IND | Domestic (VU) | 1 | 2 |
| 41 | 7K1CPT | JPN | International (DX) | 1 | 2 |
| 42 | E70Z | BIH | International (DX) | 1 | 2 |
| 43 | BD4UJ | CHN | International (DX) | 1 | 2 |
| 44 | BG7SFE | CHN | International (DX) | 1 | 2 |
| 45 | JF2VAX | JPN | International (DX) | 1 | 2 |
| 46 | VU3EEE | IND | Domestic (VU) | 1 | 2 |
| 47 | 9M2HUS | MYS | International (DX) | 1 | 2 |
| 48 | BG8KII | CHN | International (DX) | 1 | 2 |
| 49 | S21ED | BGD | International (DX) | 1 | 2 |
| 50 | VR2XAB | HKG | International (DX) | 1 | 2 |
| 51 | 9N2WF | NPL | International (DX) | 1 | 2 |
| 52 | VU3KFK | IND | Domestic (VU) | 1 | 2 |
| 53 | E20JWB | THA | International (DX) | 1 | 2 |
| 54 | 9V1AV | SGP | International (DX) | 1 | 1 |
| 55 | YC1RQZ | IDN | International (DX) | 1 | 1 |
| 56 | BG5JKZ | CHN | International (DX) | 1 | 1 |
| 57 | R3AIR | RUS | International (DX) | 1 | 1 |
| 58 | VU2TLI | IND | Domestic (VU) | 1 | 1 |
| 59 | DG0BS | DEU | International (DX) | 1 | 1 |
| 60 | JA9KRO | JPN | International (DX) | 1 | 1 |
| 61 | SP8DK | POL | International (DX) | 1 | 1 |
| 62 | EA5UI | ESP | International (DX) | 1 | 1 |
| 63 | 9K5MO | KWT | International (DX) | 1 | 1 |
| 64 | G6DXY | GBR | International (DX) | 1 | 1 |
| 65 | IU2HUQ | ITA | International (DX) | 1 | 1 |
| 66 | G0BIX | GBR | International (DX) | 1 | 1 |
| 67 | IX1CKN | ITA | International (DX) | 1 | 1 |
| 68 | OH2CGU | FIN | International (DX) | 1 | 1 |
| 69 | PA2RSD | NLD | International (DX) | 1 | 1 |
| 70 | LA9GX | NOR | International (DX) | 1 | 1 |
| 71 | ON4VT | BEL | International (DX) | 1 | 1 |
| 72 | SP8DJY | POL | International (DX) | 1 | 1 |
| 73 | DO1MDE | DEU | International (DX) | 1 | 1 |
| 74 | EA3FH | ESP | International (DX) | 1 | 1 |
| 75 | EA5HEU | ESP | International (DX) | 1 | 1 |
| 76 | EA5RJ | ESP | International (DX) | 1 | 1 |
| 77 | DD7EE | DEU | International (DX) | 1 | 1 |
| 78 | DG9XN | DEU | International (DX) | 1 | 1 |
| 79 | DL1HQY | DEU | International (DX) | 1 | 1 |
| 80 | F4IDN | FRA | International (DX) | 1 | 1 |
| 81 | EC5CSW | ESP | International (DX) | 1 | 1 |
| 82 | HS0AC | THA | International (DX) | 1 | 1 |
| 83 | OH6GAZ | FIN | International (DX) | 1 | 1 |
| 84 | PD3PAM | NLD | International (DX) | 1 | 1 |
| 85 | SP1TJ | POL | International (DX) | 1 | 1 |
| 86 | S21CAN | BGD | International (DX) | 1 | 1 |
| 87 | S21HMX | BGD | International (DX) | 1 | 1 |
| 88 | SM3NRY | SWE | International (DX) | 1 | 1 |
| 89 | IZ2QGF | ITA | International (DX) | 1 | 1 |
| 90 | JA2KVB | JPN | International (DX) | 1 | 1 |
| 91 | VK8DNT | AUS | International (DX) | 1 | 1 |
| 92 | VU2YK | IND | Domestic (VU) | 1 | 1 |
| 93 | BG5FZU | CHN | International (DX) | 1 | 1 |
| 94 | BI8AWT | CHN | International (DX) | 1 | 1 |
| 95 | YB1KK | IDN | International (DX) | 1 | 1 |
| 96 | VK6DS | AUS | International (DX) | 1 | 1 |
| 97 | VU2CW | IND | Domestic (VU) | 1 | 1 |
| 98 | VU2ZRF | IND | Domestic (VU) | 1 | 1 |
| 99 | VU3XRY | IND | Domestic (VU) | 1 | 1 |
| 100 | BH6JOG | CHN | International (DX) | 1 | 1 |
| 101 | OK2IH | CZE | International (DX) | 1 | 1 |
| 102 | DF2ET | DEU | International (DX) | 1 | 1 |
| 103 | DL1RI | DEU | International (DX) | 1 | 1 |
| 104 | EA1DR | ESP | International (DX) | 1 | 1 |
| 105 | EB3JT | ESP | International (DX) | 1 | 1 |
| 106 | F5SG | FRA | International (DX) | 1 | 1 |
| 107 | VU3FXE | IND | Domestic (VU) | 1 | 1 |
| 108 | VU2XMS | IND | Domestic (VU) | 1 | 1 |
| 109 | VU3HGZ | IND | Domestic (VU) | 1 | 1 |
| 110 | VU2MZT | IND | Domestic (VU) | 1 | 1 |
| 111 | VU3IYI | IND | Domestic (VU) | 1 | 1 |
| 112 | VU3NZZ | IND | Domestic (VU) | 1 | 1 |
| 113 | BA7LUI | CHN | International (DX) | 1 | 1 |
| 114 | BG2EWI | CHN | International (DX) | 1 | 1 |
| 115 | HS1JZT | THA | International (DX) | 1 | 1 |
| 116 | S21SES | BGD | International (DX) | 1 | 1 |
| 117 | VU2JDC | IND | Domestic (VU) | 1 | 1 |
| 118 | EA3GCT | ESP | International (DX) | 1 | 1 |
| 119 | HS5AES | THA | International (DX) | 1 | 1 |
| 120 | IW4DV | ITA | International (DX) | 1 | 1 |
| 121 | JK6DXD | JPN | International (DX) | 1 | 1 |
| 122 | DV1K | PHL | International (DX) | 1 | 1 |
| 123 | HS0GWL | THA | International (DX) | 1 | 1 |
| 124 | HS0ZQS | THA | International (DX) | 1 | 1 |
| 125 | R4GD | RUS | International (DX) | 1 | 1 |
| 126 | YD6AJA | IDN | International (DX) | 1 | 1 |
| 127 | DJ9FM | DEU | International (DX) | 1 | 1 |
| 128 | E21EIC | THA | International (DX) | 1 | 1 |
| 129 | E25RMW | THA | International (DX) | 1 | 1 |
| 130 | E25VCR | THA | International (DX) | 1 | 1 |
| 131 | HS5PPN | THA | International (DX) | 1 | 1 |
| 132 | SP9RHN | POL | International (DX) | 1 | 1 |
| 133 | R2BBX | RUS | International (DX) | 1 | 1 |
| 134 | RX3AEX | RUS | International (DX) | 1 | 1 |
| 135 | TA2R | TUR | International (DX) | 1 | 1 |
| 136 | 4S7AB | LKA | International (DX) | 1 | 1 |
| 137 | BD8FPI | CHN | International (DX) | 1 | 1 |
| 138 | BI6NSL | CHN | International (DX) | 1 | 1 |
| 139 | HS7WMU | THA | International (DX) | 1 | 1 |
| 140 | LX1NO | LUX | International (DX) | 1 | 1 |
| 141 | VU24JD | IND | Domestic (VU) | 1 | 1 |
| 142 | HS5TXB | THA | International (DX) | 1 | 1 |
Interestingly, we seem to have about 142 hunters for parks in India, and a healthy 71% are DX, and the remaining Indian hunter list is dominated by VU3TBU who has 10 unique parks himself and 26 confirmed QSOs. The next on the list is VU3JXF, who has 5 parks and 12 QSOs. The DX hunter who seems to be active is 4S6RYD with 5 parks and 5 QSOs.
Next steps
- Why are there so few parks listed in India? It is an arduous task to list and maintain the database of parks, so far the Indian park admins have been extremely responsive, but it would be great to chat with them and see if we can organise a marathon park addition process.
- It would be great to understand “who” and from which part of world are the hunters for parks from India.
- I’d love to have a chat with each of the park activators to discuss their activations, their challenges, their go-bags, and their approaches in general!
- What are the challenges with respect to the rules of POTA and the rules and regulations of Indian parks? That would be an interesting deep-dive to discuss the US/EU centrism of radio operations.
- I would love to clean up these scripts, make them “live” or the dashboard that everyone fancies nowadays rather than the static version that they currently are.
Please do head over to https://k4fmh.com/2026/01/26/a-snapshot-of-u-s-pota-sites-activators-and-activations/ and https://k4fmh.com/2026/02/08/does-potas-selection-of-u-s-park-entities-shortchange-urban-hams/ to read them (Last accessed on Thursday, 09 Jul 2026). ↩︎
All the data for this is fetched from https://pota.app/ and https://www.hamqth.com/ . The data for each image/chart is provided below the image/chart in a table. If you’d like the scripts used to generate them (for your own use of to help me improve this post), please do email me! ↩︎
I would have loved to analyse this by number of operators in each state as well, so any pointers on this data for India would be great! ↩︎
At the time of writing this post, VU2UCR is activating yet another park! ↩︎
The ranks from 1 (most activated) to 191 (least activated) came from the original sort. The cumulative percentage of the total activations that each park represents is plotted against the rank. (In statistics and economics, this is a type of Lorenz Curve.) ↩︎
At least based on the information they’ve provided on Thursday, 09 Jul 2026 at https://hfi2026.nitk.ac.in/participants. ↩︎
Data successfully aggregated and verified from the following live endpoints:
- http://wsprnet.org/archive/wsprspots-2026-05.csv.gz
- http://www.reversebeacon.net/raw_data/dl.php?f=20260712
- https://api.pota.app/park/leaderboard/IN-0041
- https://api.pota.app/park/leaderboard/IN-0042
- https://api.pota.app/park/leaderboard/IN-0043
- https://api.pota.app/park/leaderboard/IN-0044
- https://api.pota.app/park/leaderboard/IN-0052
- https://api.pota.app/park/leaderboard/IN-0053
- https://api.pota.app/park/leaderboard/IN-0054
- https://api.pota.app/park/leaderboard/IN-0055
- https://api.pota.app/park/leaderboard/IN-0056
- https://api.pota.app/park/leaderboard/IN-0057
- https://api.pota.app/park/leaderboard/IN-0058
- https://api.pota.app/park/leaderboard/IN-0059
- https://api.pota.app/park/leaderboard/IN-0060
- https://api.pota.app/park/leaderboard/IN-0061
- https://api.pota.app/park/leaderboard/IN-0062
- https://api.pota.app/park/leaderboard/IN-0067
- https://api.pota.app/park/leaderboard/IN-0076
- https://api.pota.app/park/leaderboard/IN-0081
- https://api.pota.app/park/leaderboard/IN-0085
- https://api.pota.app/park/leaderboard/IN-0100
- https://api.pota.app/park/leaderboard/IN-0101
- https://api.pota.app/park/leaderboard/IN-0103
- https://api.pota.app/park/leaderboard/IN-0107
- https://api.pota.app/park/leaderboard/IN-0113
- https://api.pota.app/park/leaderboard/IN-0126
- https://api.pota.app/park/leaderboard/IN-0127
- https://api.pota.app/park/leaderboard/IN-0128
- https://api.pota.app/park/leaderboard/IN-0135
- https://api.pota.app/park/leaderboard/IN-0136
- https://api.pota.app/park/leaderboard/IN-0137
- https://api.pota.app/park/leaderboard/IN-0138
- https://api.pota.app/park/leaderboard/IN-0139
- https://api.pota.app/park/leaderboard/IN-0140
- https://api.pota.app/park/leaderboard/IN-0142
- https://api.pota.app/park/leaderboard/IN-0144
- https://api.pota.app/park/leaderboard/IN-0145
- https://api.pota.app/park/leaderboard/IN-0154
- https://api.pota.app/park/leaderboard/IN-0155
- https://api.pota.app/park/leaderboard/IN-0157
- https://api.pota.app/park/leaderboard/IN-0158
- https://api.pota.app/park/leaderboard/IN-0159
- https://api.pota.app/park/leaderboard/IN-0169
- https://api.pota.app/park/leaderboard/IN-0170
- https://api.pota.app/park/leaderboard/IN-0176
- https://api.pota.app/park/leaderboard/IN-0181
- https://api.pota.app/park/leaderboard/IN-0182
- https://api.pota.app/park/leaderboard/IN-0183
- https://api.pota.app/park/leaderboard/IN-0184
- https://api.pota.app/park/leaderboard/IN-0185
- https://api.pota.app/park/leaderboard/IN-0189
- https://api.pota.app/program/parks/IN
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- https://cqwpx.com/publiclogs/2026cw/
- https://cqwpx.com/publiclogs/2026ph/
- https://cqwpxrtty.com/logs_received.htm
- https://cqwpxrtty.com/publiclogs/2024/
- https://cqwpxrtty.com/publiclogs/2025/
- https://cqww.com/logs_received_cw.htm
- https://cqww.com/logs_received_ssb.htm
- https://cqww.com/publiclogs/2024cw/
- https://cqww.com/publiclogs/2024ph/
- https://cqww.com/publiclogs/2025cw/
- https://cqww.com/publiclogs/2025ph/
- https://cqwwrtty.com/logs_received.htm
- https://cqwwrtty.com/publiclogs/2024/
- https://cqwwrtty.com/publiclogs/2025/
- https://lotw.arrl.org/lotw-user-activity.csv
- https://retrieve.pskreporter.info/query?senderCallsign=VU*&lastXSeconds=86400
To avoid hitting severe API rate limits or being IP-blocked by these services, this data represents a rolling 24-hour to 30-day snapshot, unlike the 3-year baseline of the contest data above. ↩︎
POTA rules state 10 successful QSOs across all bands and modes put together on a single day (UTC time) as a successful activation. Whereas World Wide Flora & Fauna requires 44 activations at the same location spread over a number of days. ↩︎
It would have been great to plot this on a map too, but we don’t have the data for it. ↩︎
Please note that the license classes/categories in India have undergone numerous changes, for the purposes of this post, I have simply categorised them based on the fact that a VU2 prefix is a general grade operator, whereas a VU3 prefix is a restricted grade operator. ↩︎
Especially if new operators turn up, or there is an uptake in “outdoor” HF activity. ↩︎
Polar bearing chart tracks the physical exploration footprints of Indian POTA activators. Radially bound straight lines map out localized activations, while multi-point runs expand dynamically into continuous filled amoeboid boundaries that encapsulate total operational theater coverage. ↩︎