ÀϺ»ÀÇ Áö¸®°ø°£ ºÐ¼® : ½ÃÀå Á¡À¯À² ºÐ¼®, »ê¾÷ µ¿Çâ ¹× Åë°è, ¼ºÀå ¿¹Ãø(2024-2029³â)
Japan Geospatial Analytics - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2024 - 2029)
»óǰÄÚµå
:
1549972
¸®¼Ä¡»ç
:
Mordor Intelligence Pvt Ltd
¹ßÇàÀÏ
:
2024³â 09¿ù
ÆäÀÌÁö Á¤º¸
:
¿µ¹®
¶óÀ̼±½º & °¡°Ý (ºÎ°¡¼¼ º°µµ)
¤± Add-on °¡´É: °í°´ÀÇ ¿äû¿¡ µû¶ó ÀÏÁ¤ÇÑ ¹üÀ§ ³»¿¡¼ CustomizationÀÌ °¡´ÉÇÕ´Ï´Ù. ÀÚ¼¼ÇÑ »çÇ×Àº ¹®ÀÇÇØ Áֽñ⠹ٶø´Ï´Ù.
¤± º¸°í¼¿¡ µû¶ó ÃֽŠÁ¤º¸·Î ¾÷µ¥ÀÌÆ®ÇÏ¿© º¸³»µå¸³´Ï´Ù. ¹è¼Û±âÀÏÀº ¹®ÀÇÇØ Áֽñ⠹ٶø´Ï´Ù.
ÀϺ»ÀÇ Áö¸®°ø°£ ºÐ¼® ½ÃÀå ±Ô¸ð´Â 2024³â 15¾ï 3,000¸¸ ´Þ·¯·Î ÃßÁ¤µÇ¸ç, 2029³â¿¡´Â 29¾ï 4,000¸¸ ´Þ·¯¿¡ ´ÞÇÒ °ÍÀ¸·Î ¿¹»óµÇ¸ç, ¿¹Ãø ±â°£(2024-2029³â) µ¿¾È 13.95%ÀÇ CAGR·Î ¼ºÀåÇÒ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù.
ÁÖ¿ä ÇÏÀ̶óÀÌÆ®
- Áö¸®°ø°£ ºÐ¼®Àº GPS, À§¼º»çÁø µî Áö¸®Á¤º¸ ½Ã½ºÅÛ(GIS) µ¥ÀÌÅÍ¿Í À̹ÌÁö¸¦ ¼öÁý, Á¶ÀÛ, Ç¥½ÃÇÏ´Â °úÁ¤ÀÔ´Ï´Ù. ÀϺ»ÀÇ Áö¸®°ø°£ ºÐ¼® ½ÃÀåÀÇ Àü³âµµ ½ÃÀå ±Ô¸ð´Â 14¾ï ´Þ·¯·Î ÇâÈÄ 5³â°£ ¿¬Æò±Õ 12.69%ÀÇ CAGRÀ» ±â·ÏÇϸç 28¾ï 1,000¸¸ ´Þ·¯¿¡ ´ÞÇÒ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù.
- ÀϺ»ÀÇ Áö¸®°ø°£ ºÐ¼® ½ÃÀåÀº ½º¸¶Æ® ½ÃƼ °³¹ß ÇÁ·ÎÁ§Æ® äÅÃÀÌ Áõ°¡ÇÔ¿¡ µû¶ó Å©°Ô ¼ºÀåÇϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ³ë·ÂÀº µµ½Ã ÀÎÇÁ¶ó¸¦ °ÈÇϰí, °ø°ø ¼ºñ½º¸¦ °³¼±Çϸç, ÀÚ¿ø °ü¸®¸¦ ÃÖÀûÈÇϱâ À§ÇØ Ã·´Ü ±â¼úÀ» Ȱ¿ëÇϰí ÀÖ½À´Ï´Ù. ÀϺ» µµ½ÃµéÀº Áö¸®Àû °ø°£ ºÐ¼®À» °èȹ°ú ¿î¿µ¿¡ ÅëÇÕÇÔÀ¸·Î½á °ø°£ µ¥ÀÌÅ͸¦ ´õ Àß ºÐ¼®Çϰí, ȯ°æ º¯È¸¦ ¸ð´ÏÅ͸µÇϰí, Á¤º¸¿¡ ÀÔ°¢ÇÑ ÀÇ»ç°áÁ¤À» ³»¸± ¼ö ÀÖ°Ô µÇ¾ú½À´Ï´Ù. ÀÌ·¯ÇÑ Ãß¼¼´Â ¾ÕÀ¸·Îµµ ½ÃÀåÀ» ÁÖµµÇϰí Çõ½Å°ú ÅõÀÚ¿¡ ´ëÇÑ ¼ö¸¹Àº ±âȸ¸¦ Á¦°øÇÒ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù.
- ¿¹¸¦ µé¾î, Á¶Áö¾Æ°ø°ú´ëÇаú µµÄì´ëÇÐÀÇ °øµ¿ ¿¬±¸ÀÎ µµÄì ½º¸¶Æ® ½ÃƼ ½ºÆ©µð¿À´Â µµÄìÀÇ ½º¸¶Æ®ÇÏ°í »ýÅÂÀûÀÎ Ä¿¹Â´ÏƼ °³¹ßÀ» ¸ñÇ¥·Î ÇÏ´Â ¿¬±¸ ÇÁ·Î±×·¥ÀÔ´Ï´Ù. ÀÌ ÇÁ·Î±×·¥Àº Áö¸®Àû °ø°£ ºÐ¼® ¹× ±âŸ ±â¼úÀ» Ȱ¿ëÇÏ¿© µµ½Ã °èȹ, ±³Åë, ¿¡³ÊÁö ¼Òºñ µîÀÇ µ¥ÀÌÅ͸¦ ºÐ¼® ¹× ½Ã°¢ÈÇÕ´Ï´Ù.
- ¶ÇÇÑ 5G ±â¼úÀÇ º¸±ÞÀº ÀϺ»ÀÇ Áö¸®°ø°£ ºÐ¼® ½ÃÀåÀÇ ¼ö¿ä¿Í ¼ºÀåÀ» Å©°Ô °ÈÇϰí ÀÖÀ¸¸ç, 5GÀÇ µîÀåÀ¸·Î Áö¸®°ø°£ ºÐ¼®ÀÇ ±â´ÉÀÌ È®ÀåµÇ¾î ´õ ºü¸¥ µ¥ÀÌÅÍ Ã³¸®, ´õ ³ôÀº Á¤È®µµ, ½Ç½Ã°£ ºÐ¼® ±â´ÉÀ» Á¦°øÇϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ±â¼ú ¹ßÀüÀ¸·Î ±³Åë, µµ½Ã °èȹ, Àç³ °ü¸® µî ´Ù¾çÇÑ ºÐ¾ß¿¡¼ Áö°ø°£ µ¥ÀÌÅ͸¦ º¸´Ù È¿°úÀûÀ¸·Î Ȱ¿ëÇÒ ¼ö ÀÖ°Ô µÇ¾ú½À´Ï´Ù. ±× °á°ú ÀϺ»ÀÇ Áö¸®°ø°£ ºÐ¼® ½ÃÀåÀº 5G ±â¼ú äÅà Ȯ´ë¿¡ ÈûÀÔ¾î °·ÂÇÑ ¼ºÀå¼¼¸¦ º¸À̰í ÀÖ½À´Ï´Ù.
- ÀϺ»¿¡¼´Â ¸î °¡Áö ¿äÀÎÀÌ Áö¸®°ø°£ ºÐ¼® ½ÃÀåÀ» °ßÀÎÇϰí ÀÖÁö¸¸, ³ôÀº ºñ¿ë°ú ¿î¿µ»óÀÇ ¾î·Á¿òÀ¸·Î ÀÎÇØ ¼ö¿ä¿Í ¼ºÀå¿¡ Å« Á¦¾àÀÌ µÇ°í ÀÖ½À´Ï´Ù. ¼ÒÇÁÆ®¿þ¾î, Çϵå¿þ¾î, ¼÷·ÃµÈ Àη µî Áö¸®°ø°£ ºÐ¼® ¼Ö·ç¼Ç µµÀÔ¿¡ µû¸¥ ³ôÀº ºñ¿ëÀº ¸¹Àº Á¶Á÷¿¡ Å« À庮À¸·Î ÀÛ¿ëÇϰí ÀÖ½À´Ï´Ù. ¶ÇÇÑ, µ¥ÀÌÅÍ ÇÁ¶óÀ̹ö½Ã ¹®Á¦, ÅëÇÕÀÇ º¹À⼺, Áö¼ÓÀûÀÎ ¾÷µ¥ÀÌÆ® ¹× À¯Áöº¸¼öÀÇ Çʿ伺°ú °°Àº ¿î¿µ»óÀÇ ¹®Á¦µµ ½ÃÀå È®´ë¿¡ °É¸²µ¹·Î ÀÛ¿ëÇϰí ÀÖ½À´Ï´Ù.
ÀϺ»ÀÇ Áö¸®°ø°£ ºÐ¼® ½ÃÀå µ¿Çâ
Àç³ À§Çè °æ°¨ ¹× °ü¸®
- ÀϺ»¿¡¼ ÀÚ¿¬ÀçÇØÀÇ ºóµµ¿Í °µµ°¡ Áõ°¡ÇÔ¿¡ µû¶ó °í±Þ Áö¸®°ø°£ ºÐ¼®ÀÇ µµÀÔÀÌ ÇÊ¿äÇÏ°Ô µÇ¾ú½À´Ï´Ù. ÀÌ·¯ÇÑ µµ±¸´Â ÀÌ·¯ÇÑ »ç°ÇÀÇ ¿µÇâÀ» ¿¹Ãø, ´ëºñ ¹× ¿ÏÈÇÏ´Â µ¥ µµ¿òÀÌ µÇ¾î ÀϺ»ÀÇ Àç³ º¹¿ø·ÂÀ» Çâ»ó½ÃŰ´Â µ¥ µµ¿òÀÌ µÇ°í ÀÖ½À´Ï´Ù. ¿¹¸¦ µé¾î, Áö¸®°ø°£ ºÐ¼®Àº ÀáÀçÀûÀÎ Àç³ ½Ã³ª¸®¿À¸¦ ¸ðµ¨¸µÇÏ¿© ´ç±¹ÀÌ º¸´Ù È¿°úÀûÀÎ ´ëÇÇ °èȹ°ú ÀÚ¿ø ¹èºÐ Àü·«À» ¼ö¸³ÇÒ ¼ö ÀÖµµ·Ï µ½½À´Ï´Ù.
- ¶ÇÇÑ, ÀçÇØ À§Çè °¨¼Ò¸¦ À§ÇÑ Á¤ºÎÀÇ ³ë·Â°ú Á¤Ã¥µµ Áö¸®°ø°£ ºÐ¼® ½ÃÀåÀÇ ¼ºÀå¿¡ Å©°Ô ±â¿©Çϰí ÀÖ½À´Ï´Ù. ÀϺ» Á¤ºÎ´Â Àç³ °ü¸® Àü·«À» °³¼±Çϱâ À§ÇØ Ã·´Ü ±â¼ú¿¡ ÅõÀÚÇϰí ÀÖÀ¸¸ç, ÀÌ´Â Áö¸®°ø°£ ºÐ¼®¿¡ ´ëÇÑ ¼ö¿ä¸¦ ÃËÁøÇϰí ÀÖ½À´Ï´Ù. ±¹°¡ ȸº¹Åº·Â¼º ÇÁ·Î±×·¥, ¹æÀç±âº»°èȹ µîÀÇ ÇÁ·Î±×·¥Àº Àç³ ´ëºñ¿Í ´ëÀÀÀ» °ÈÇϱâ À§ÇØ ÁöÇü°ø°£ µ¥ÀÌÅÍ ÅëÇÕÀ» °Á¶Çϰí ÀÖ½À´Ï´Ù.
- ¶ÇÇÑ, Áö¸®Àû °ø°£ ºÐ¼®°ú ÀΰøÁö´É ¹× ºòµ¥ÀÌÅÍ¿Í °°Àº ´Ù¸¥ ±â¼ú°úÀÇ ÅëÇÕÀº Àç³ °ü¸® ºÐ¾ß¿¡¼ »õ·Î¿î Çõ½ÅÀÇ ±âȸ¸¦ âÃâÇϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ÅëÇÕÀº È¿°úÀûÀÎ Àç³ ´ëÀÀ°ú º¹±¸¿¡ ÇʼöÀûÀÎ º¸´Ù Á¤È®ÇÏ°í ½Ã±âÀûÀýÇÑ µ¥ÀÌÅÍ ºÐ¼®À» °¡´ÉÇÏ°Ô ÇÕ´Ï´Ù. ¿¹¸¦ µé¾î, AI ¾Ë°í¸®ÁòÀº Áö¸®Àû °ø°£ µ¥ÀÌÅ͸¦ ºÐ¼®ÇÏ¿© ÅÂdzÀÇ °æ·Î¸¦ ¿¹ÃøÇÏ¿© º¸´Ù ½Å¼ÓÇϰí Á¤È®ÇÑ ºñ»ó ´ëÀÀÀ» °¡´ÉÇÏ°Ô ÇÕ´Ï´Ù.
- 2023³â 9¿ù, ÀϺ» È÷·Î½Ã¸¶Çö¿¡ À§Ä¡ÇÑ È÷°¡½ÃÈ÷·Î½Ã¸¶½Ã´Â ASP.NET¿ë TatukGIS °³¹ßÀÚ Ä¿³Î(DK)·Î ±¸µ¿µÇ´Â ÀÌ Ç÷§ÆûÀº ´Ù¼öÀÇ ³ó¾÷¿ë Àú¼öÁö¿Í °ü·ÃµÈ È«¼ö À§ÇèÀ» Æò°¡ÇÒ ¼ö ÀÖµµ·Ï ¼³°èµÇ¾ú½À´Ï´Ù. Æò°¡ÇÒ ¼ö ÀÖµµ·Ï ¼³°èµÇ¾ú½À´Ï´Ù.
³ô¾ÆÁö´Â À§Ä¡ Á¤º¸ ¼ºñ½º(LBS) ¼ö¿ä
- ÀϺ»¿¡¼ À§Ä¡ Á¤º¸ ¼ºñ½º(LBS)ÀÇ ÀαⰡ ³ô¾ÆÁö¸é¼ Áö¸®°ø°£ ºÐ¼® ½ÃÀå È®´ë¿¡ Å©°Ô ±â¿©Çϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ¼ºñ½º´Â ½Ç½Ã°£ Áö¸® µ¥ÀÌÅ͸¦ Ȱ¿ëÇÏ¿© Á¤º¸, ¿£ÅÍÅ×ÀÎ¸ÕÆ®, º¸¾ÈÀ» Á¦°øÇÏ°í »ç¿ëÀÚ °æÇè°ú ¾÷¹« È¿À²¼ºÀ» Çâ»ó½Ãŵ´Ï´Ù. ³»ºñ°ÔÀ̼Ç, ¼Ò¼È ³×Æ®¿öÅ·, ¸ð¹ÙÀÏ ¸¶ÄÉÆÃ µî ´Ù¾çÇÑ ¾ÖÇø®ÄÉÀ̼ǿ¡ LBS°¡ ÅëÇÕµÇ¸é¼ Áö¸®°ø°£ ºÐ¼® ¼Ö·ç¼Ç¿¡ ´ëÇÑ ¼ö¿ä°¡ ±ÞÁõÇϰí ÀÖ½À´Ï´Ù.
- ¶ÇÇÑ, ±â¼ú ¹ßÀüÀº À§Ä¡Á¤º¸ ¼ºñ½ºÀÇ º¸±Þ¿¡ Áß¿äÇÑ ¿ªÇÒÀ» Çϰí ÀÖ½À´Ï´Ù. ½º¸¶Æ®ÆùÀÇ º¸±Þ°ú Ãʰí¼Ó ÀÎÅÍ³Ý ÀÎÇÁ¶ó ±¸ÃàÀ¸·Î ¼ÒºñÀÚ¿Í ±â¾÷ÀÌ LBS¿¡ ½±°Ô Á¢±ÙÇϰí Ȱ¿ëÇÒ ¼ö ÀÖ°Ô µÇ¾úÀ¸¸ç, 2023³â ÀϺ»ÀÇ ½º¸¶Æ®Æù º¸À¯À²Àº ¾à 79%·Î 2010³â´ë Áß¹Ý ÀÌÈÄ Å« ÆøÀ¸·Î »ó½ÂÇß½À´Ï´Ù. ÀÌ·¯ÇÑ ±â¼ú ¹ßÀüÀº º¸´Ù Á¤È®ÇÏ°í ½Å·ÚÇÒ ¼ö ÀÖ´Â ÁöÇü °ø°£ µ¥ÀÌÅÍ ¼öÁýÀ» °¡´ÉÇÏ°Ô ÇÏ¿© ÀϺ»ÀÇ Áö¸®°ø°£ ºÐ¼® ½ÃÀåÀ» ´õ¿í Ȱ¼ºÈ½ÃÄ×½À´Ï´Ù.
- ¶ÇÇÑ, ÀϺ» Á¤ºÎ´Â ´Ù¾çÇÑ °ø°ø ¼ºñ½º ¹× µµ½Ã °èȹ ±¸»ó¿¡ Áö°ø°£ Á¤º¸ Ȱ¿ëÀ» Àû±ØÀûÀ¸·Î ÃßÁøÇϰí ÀÖ½À´Ï´Ù. ÁöÇü°ø°£ µ¥ÀÌÅÍ ÀÎÇÁ¶ó¸¦ °³¼±Çϱâ À§ÇÑ Á¤ºÎÀÇ Á¤Ã¥°ú ÅõÀÚ´Â Áö¸®°ø°£ ºÐ¼® ½ÃÀå ¼ºÀå¿¡ À¯¸®ÇÑ È¯°æÀ» Á¶¼ºÇϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ³ë·ÂÀº ´Ù¾çÇÑ ºÐ¾ß¿¡¼ ÁöÇü°ø°£ ±â¼úÀÇ Çõ½Å°ú äÅÃÀ» ´õ¿í ÃËÁøÇÒ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù.
- ¶ÇÇÑ, ¿î¼Û, ¹°·ù, ¼Ò¸Å »ê¾÷¿¡¼ Áö¸®°ø°£ ºÐ¼®ÀÇ È°¿ëÀÌ È®´ëµÇ°í ÀÖ´Â °Íµµ ½ÃÀå ¼ºÀå¿¡ ±â¿©Çϰí ÀÖ½À´Ï´Ù. ÀÌµé ºÐ¾ßÀÇ ±â¾÷µéÀº Áö¸®°ø°£ µ¥ÀÌÅ͸¦ Ȱ¿ëÇÏ¿© °æ·Î¸¦ ÃÖÀûÈÇϰí, ÀÚ»êÀ» °ü¸®Çϸç, °í°´ °æÇèÀ» °³¼±Çϰí ÀÖ½À´Ï´Ù. °ø°£ µ¥ÀÌÅÍ ºÐ¼® ¹× ½Ã°¢È´Â ±â¾÷ÀÌ Á¤º¸¿¡ ÀÔ°¢ÇÑ ÀÇ»ç°áÁ¤À» ³»¸®°í ¿î¿µ È¿À²¼ºÀ» °³¼±ÇÏ´Â µ¥ µµ¿òÀÌ µÇ´Â ±ÍÁßÇÑ ÀλçÀÌÆ®¸¦ Á¦°øÇÕ´Ï´Ù.
ÀϺ»ÀÇ Áö¸®°ø°£ ºÐ¼® »ê¾÷ °³¿ä
ÀϺ»ÀÇ Áö¸®°ø°£ ºÐ¼® ½ÃÀåÀº Hexagon AB¿Í °°Àº ´ë±â¾÷ÀÌ Áö¹èÇϰí ÀÖÁö¸¸, ÅëÇÕÀÇ Ãß¼¼¸¦ ¸ñ°ÝÇϰí ÀÖ½À´Ï´Ù. À̵é ÁÖ¿ä ±â¾÷µéÀº ½ÃÀå¿¡¼ÀÇ ÀÔÁö¸¦ °ÈÇϱâ À§ÇØ Àü·«ÀûÀ¸·Î Á¦ÈÞ ¹× Àμö¸¦ ÃßÁøÇϰí ÀÖ½À´Ï´Ù. µ¿½Ã¿¡ ÷´Ü ÁöÇü °ø°£ µµ±¸¸¦ °³¹ßÇϱâ À§ÇØ ±â¼ú Çõ½Å, »õ·Î¿î ¾ÖÇø®ÄÉÀÌ¼Ç °³¹ß ¹× ¿¬±¸ °³¹ß¿¡ ÁýÁßÇϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ µµ±¸´Â µµ½Ã °èȹ, Àç³ °ü¸®, ȯ°æ ¸ð´ÏÅ͸µ µî ´Ù¾çÇÑ ºÐ¾ß¿¡ Àû¿ëµÇ°í ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ³ë·ÂÀº ÇâÈÄ ¸î ³â µ¿¾È ½ÃÀå ¼ºÀåÀ» ÃËÁøÇÒ °ÍÀÔ´Ï´Ù.
2024³â 1¿ù, AWS´Â 2027³â±îÁö ÀϺ»¿¡ 2Á¶ 2,600¾ï ¿£(152¾ï 4,000¸¸ ´Þ·¯)À» ÅõÀÚÇÏ¿© ÀϺ» Ŭ¶ó¿ìµå ÄÄÇ»ÆÃ ÀÎÇÁ¶ó¸¦ È®ÀåÇÒ °èȹÀ» ¹ßÇ¥ÇßÀ¸¸ç, AWS´Â µµÄì¿Í ¿À»çÄ«ÀÇ ½Ã¼³À» È®ÀåÇÏ´Â °Í ¿Ü¿¡µµ AWSÀÇ ±³À° ÇÁ·Î±×·¥, ±³À°, ÈÆ·Ã, ÀÎÁõÀ» ÅëÇØ µðÁöÅÐ ±â¼ú °ÝÂ÷¸¦ ÇØ¼ÒÇÔÀ¸·Î½á ÀϺ»ÀÇ Å¬¶ó¿ìµå ÀáÀç·ÂÀ» ²ø¾î³¾ ¼ö ÀÖµµ·Ï Áö¿øÇÒ °èȹÀÔ´Ï´Ù.
±âŸ ÇýÅÃ
- ¿¢¼¿ Çü½ÄÀÇ ½ÃÀå ¿¹Ãø(ME) ½ÃÆ®
- 3°³¿ù°£ÀÇ ¾Ö³Î¸®½ºÆ® Áö¿ø
¸ñÂ÷
Á¦1Àå ¼Ò°³
- Á¶»ç °¡Á¤°ú ½ÃÀå Á¤ÀÇ
- Á¶»ç ¹üÀ§
Á¦2Àå ÁÖ¿ä ¿ä¾à
Á¦3Àå Á¶»ç ¹æ¹ý
Á¦4Àå ½ÃÀå ÀλçÀÌÆ®
- ½ÃÀå °³¿ä
- ¾÷°èÀÇ ¸Å·Â - Porter's Five Forces ºÐ¼®
- ½Å±Ô Âü¿©¾÷üÀÇ À§Çù
- ±¸¸ÅÀÚÀÇ ±³¼··Â
- °ø±Þ ±â¾÷ÀÇ ±³¼··Â
- ´ëüǰÀÇ À§Çù
- °æÀï ±â¾÷ °£ÀÇ °æÀï °µµ
- »ê¾÷ ¹ë·ùüÀÎ ºÐ¼®
- °Å½Ã°æÁ¦ ¿äÀÎÀÌ ½ÃÀå¿¡ ¹ÌÄ¡´Â ¿µÇâ
Á¦5Àå ½ÃÀå ¿ªÇÐ
- ½ÃÀå ¼ºÀå ÃËÁø¿äÀÎ
- ½º¸¶Æ® ½ÃƼ °³¹ß ä¿ë Áõ°¡
- ½ÃÀå ¼ºÀåÀ» ÃËÁøÇÏ´Â 5G ¼Ò°³
- ½ÃÀå ¼ºÀå ¾ïÁ¦¿äÀÎ
- °íºñ¿ë°ú ¿î¿µ»ó ¿ì·Á
- ¹ýÀû Çãµé
Á¦6Àå ½ÃÀå ¼¼ºÐÈ
- À¯Çüº°
- ¼Çǽº ºÐ¼®
- ³×Æ®¿öÅ© ÇØ¼®
- Áö¿À ½Ã°¢È
- ÃÖÁ¾ ÀÌ¿ë ¾÷°èº°
- ³ó¾÷
- À¯Æ¿¸®Æ¼¿Í Åë½Å
- ¹æÀ§¡¤Á¤º¸
- Á¤ºÎ±â°ü
- ±¤¾÷¡¤Ãµ¿¬ÀÚ¿ø
- ÀÚµ¿Â÷¡¤¿î¼Û
- ÇコÄɾî
- ºÎµ¿»ê¡¤°Ç¼³
- ±âŸ ÃÖÁ¾ ÀÌ¿ë ¾÷°èº°
Á¦7Àå °æÀï »óȲ
- ±â¾÷ °³¿ä
- Alteryx
- Hexagon AB
- TomTom
- Maxar Technologies
- Trimble
- ESRI
- Caliper Corporation
- General Electric
- Bentley Systems Co.
- Fugro
Á¦8Àå ÅõÀÚ ºÐ¼®
Á¦9Àå ½ÃÀå ±âȸ¿Í ÇâÈÄ µ¿Çâ
ksm
¿µ¹® ¸ñÂ÷
The Japan Geospatial Analytics Market size is estimated at USD 1.53 billion in 2024, and is expected to reach USD 2.94 billion by 2029, growing at a CAGR of 13.95% during the forecast period (2024-2029).
Key Highlights
- Geospatial analytics is the process of gathering, manipulating, and displaying geographic information system (GIS) data and imagery, including GPS and satellite photographs. The Japanese geospatial analytics market was valued at USD 1.40 billion in the previous year, and it is expected to register a CAGR of 12.69%, reaching USD 2.81 billion by the next five years.
- The Japanese geospatial analytics market is experiencing significant growth due to the increasing adoption of smart city development projects. These initiatives leverage advanced technologies to enhance urban infrastructure, improve public services, and optimize resource management. As cities in Japan integrate geospatial analytics into their planning and operations, they better analyze spatial data, monitor environmental changes, and make informed decisions. This trend is expected to continue driving the market forward, offering numerous opportunities for innovation and investment in the coming years.
- For instance, the Tokyo Smart City Studio, a collaboration between the Georgia Institute of Technology and the University of Tokyo, is a research program that aims to develop a smart and ecologically sound community in Tokyo. The program uses geospatial analytics and other technologies to analyze and visualize urban planning, transportation, energy consumption, and more data.
- Also, the deployment of 5G technology is significantly enhancing the demand and growth of the geospatial analytics market in Japan. With the advent of 5G, the capabilities of geospatial analytics are expanding, offering faster data processing, improved accuracy, and real-time analytics. This technological advancement enables various sectors, including transportation, urban planning, and disaster management, to leverage geospatial data more effectively. As a result, the geospatial analytics market in Japan is experiencing robust growth, driven by the increased adoption of 5G technology.
- In Japan, while several factors propel the geospatial analytics market, high costs and operational challenges are significant constraints that impede its demand and growth. The high costs associated with implementing geospatial analytics solutions, including software, hardware, and skilled personnel, pose a substantial barrier for many organizations. Additionally, operational concerns such as data privacy issues, integration complexities, and the need for continuous updates and maintenance further hinder the market's expansion.
Japan Geospatial Analytics Market Trends
Disaster Risk Reduction and Management
- The increasing frequency and intensity of natural disasters in Japan have necessitated the adoption of advanced geospatial analytics. These tools help predict, prepare for, and mitigate the impacts of such events, thereby enhancing the country's disaster resilience. For instance, geospatial analytics can model potential disaster scenarios, allowing authorities to develop more effective evacuation plans and resource allocation strategies.
- Also, government initiatives and policies aimed at disaster risk reduction significantly contribute to the growth of the geospatial analytics market. The Japanese government has been investing in advanced technologies to improve disaster management strategies, which drives the demand for geospatial analytics. Programs such as the National Resilience Program and the Basic Plan for Disaster Risk Reduction emphasize the integration of geospatial data to enhance disaster preparedness and response.
- Also, the integration of geospatial analytics with other technologies, such as artificial intelligence and big data, is creating new opportunities for innovation in disaster management. This integration allows for more accurate and timely data analysis, crucial for effective disaster response and recovery. For example, AI algorithms are used to analyze geospatial data to predict the path of a typhoon, enabling quicker and more precise emergency responses.
- In September 2023, Higashihiroshima City, located in the Hiroshima Prefecture of Japan, launched an advanced web GIS application for flood hazard and disaster prevention. This platform, powered by the TatukGIS developer kernel (DK) for ASP.NET, is engineered to assess flood risks associated with numerous agricultural irrigation reservoirs.
Growing Demand for Location-based Services
- Location-based services (LBS) have become increasingly popular in Japan, contributing significantly to the expansion of the geospatial analytics market. These services utilize real-time geographical data to provide information, entertainment, and security, enhancing user experience and operational efficiency. The integration of LBS in various applications, such as navigation, social networking, and mobile marketing, has led to a surge in demand for geospatial analytics solutions.
- Moreover, technological advancements have played a crucial role in the proliferation of location-based services. The widespread adoption of smartphones and the development of high-speed internet infrastructure have made it easier for consumers and businesses to access and utilize LBS. In 2023, smartphone ownership in Japan surged to nearly 79%, marking a substantial rise from the mid-2010s. This technological progress has enabled more precise and reliable geospatial data collection, further boosting the geospatial analytics market in Japan.
- Additionally, the Japanese government has been actively promoting the use of geospatial information for various public services and urban planning initiatives. Government policies and investments to improve geospatial data infrastructure have created a favorable environment for the growth of the geospatial analytics market. These initiatives are expected to drive further innovation and adoption of geospatial technologies across different sectors.
- Also, the increasing use of geospatial analytics in transportation, logistics, and retail industries contributes to market growth. Companies in these sectors are leveraging geospatial data to optimize routes, manage assets, and enhance customer experiences. Analyzing and visualizing spatial data provides valuable insights that help businesses make informed decisions and improve operational efficiency.
Japan Geospatial Analytics Industry Overview
The Japanese geospatial analytics market, dominated by major players like Hexagon AB, is witnessing a trend toward consolidation. These key players are strategically partnering and acquiring to fortify their market presence. Simultaneously, they innovate, roll out new applications, and focus on R&D to craft advanced geospatial tools. These tools are tailored for diverse sectors, spanning urban planning, disaster management, and environmental monitoring. Such initiatives are poised to propel market growth in the coming years.
In January 2024, AWS announced plans to invest JPY 2.26 trillion (USD 15.24 billion) in Japan by 2027 to expand cloud computing infrastructure in the country. In addition to expanding its facilities in Tokyo and Osaka, AWS planned to help Japan unlock its cloud potential by addressing the digital skills gap through AWS education programs, training, and certification.
Additional Benefits:
- The market estimate (ME) sheet in Excel format
- 3 months of analyst support
TABLE OF CONTENTS
1 INTRODUCTION
- 1.1 Study Assumptions and Market Definition
- 1.2 Scope of the Study
2 EXECUTIVE SUMMARY
3 RESEARCH METHODOLOGY
4 MARKET INSIGHTS
- 4.1 Market Overview
- 4.2 Industry Attractiveness - Porter's Five Forces Analysis
- 4.2.1 Threat of New Entrants
- 4.2.2 Bargaining Power of Buyers
- 4.2.3 Bargaining Power of Suppliers
- 4.2.4 Threat of Substitute Products
- 4.2.5 Intensity of Competitive Rivalry
- 4.3 Industry Value Chain Analysis
- 4.4 Impact of Macroeconomic Factors on Market
5 MARKET DYNAMICS
- 5.1 Market Drivers
- 5.1.1 Increase In Adoption of Smart City Development
- 5.1.2 Introduction of 5G to Boost Market Growth
- 5.2 Market Restraints
- 5.2.1 High Costs and Operational Concerns
- 5.2.2 Legal Hurdles
6 MARKET SEGMENTATION
- 6.1 By Type
- 6.1.1 Surface Analysis
- 6.1.2 Network Analysis
- 6.1.3 Geovisualization
- 6.2 By End-user Vertical
- 6.2.1 Agricultural
- 6.2.2 Utility and Communication
- 6.2.3 Defense and Intelligence
- 6.2.4 Government
- 6.2.5 Mining and Natural Resources
- 6.2.6 Automotive and Transportation
- 6.2.7 Healthcare
- 6.2.8 Real Estate and Construction
- 6.2.9 Other End-user Verticals
7 COMPETITIVE LANDSCAPE
- 7.1 Company Profiles
- 7.1.1 Alteryx
- 7.1.2 Hexagon AB
- 7.1.3 TomTom
- 7.1.4 Maxar Technologies
- 7.1.5 Trimble
- 7.1.6 ESRI
- 7.1.7 Caliper Corporation
- 7.1.8 General Electric
- 7.1.9 Bentley Systems Co.
- 7.1.10 Fugro
8 INVESTMENT ANALYSIS
9 MARKET OPPORTUNITIES AND FUTURE TRENDS
°ü·ÃÀÚ·á