20211229 19035700 1.0 TRUE TRUE HistoricLS DOGAMI compiled this data from several datasets including DOGAMI SP-34 (landslide points from 1996-1997). Each feature class dataset includes 23 fields. The dataset is a compilation of center points of published or known historic (roughly 1849-2013 in Oregon) landslides and any available information about these landslides including damage and losses and several other attributes. in report). The structure and content of the file feature classes are also detailed in Appendix A of the SLIDO report Data source: SLIDO 3.4 DOGAMI compiled this data from several datasets including DOGAMI SP-34 (landslide points from 1996-1997). Each feature class dataset includes 23 fields. The dataset is a compilation of center points of published or known historic (roughly 1849-2013 in Oregon) landslides and any available information about these landslides including damage and losses and several other attributes. in report). The structure and content of the file feature classes are also detailed in Appendix A of the SLIDO report Data source: SLIDO 3.4 DOGAMI LUP /9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADIASwDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii gAooooAKKKKACiiigAoqvc31rZtEtzOkRlbZGGONx9BVigAooooAKKKKACiiigAooooAKKKKACii igAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACsvX9QvtM0p7jTt Pe/uQQFhTqc961KKAPPdefV9du7BD4YcS216pWWZzsC4OW4xXc6hNcW2nTzWsH2i4RCyRZxvOOBV TU9estMiuC5aaaBA7wQjdJgnGQPxrQglE9vFMqsokQMAwwRkZ5oAjsZp57GGW5g8iZ1BeLOdp9Ks UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQBn3MWl6bcSatciC3lZRG9w5C8Z4GT71yPiLxHqfhLxJFqd3L5/hm6VEZgm fs7HjII7Hrz61pfEiNZ/BN5bm3mmklKpGsIywbPB/Sua1LS/GGq6NBoM1lAdKkhhieUyfvUG1dxI x2OeM9qAPTYJ4rmCOeFw8UihlYHgipK47QLrSvCl3a+EIppHZYjIJZHXA6Db1z+FdZPcwWsfmTyp EmQNznAyaAJaKzYotU/t2SZ7iE6WYgI4gvzh/XPpU2p6pZ6Rafar2Xy4dwXdgnknA6UAXKKRWDqG U5BGRS0AFFFFABRRRQAUUUUAFFFFABRRRQAUVDdXdvZW7T3MyRRKMlnOAK4mb4raNJDK+l2l/qO0 YR4IcoxzgDOaAO6kkSKNpJGCooyWJwBWZH4l0eXWBpKX8JviMrFuGW4zx68Vy9po+veMLaQ+KAtp YOyvDawMQ+P9o/jXT2vhnRrO7gu4dPhW6gQRxzbfmCgYxn6UAa1FFFABRRRQAUUUUAFFFFABRRRQ AUUUUAFFFFABRRRQAVjeKNHutd0OXT7TUJLGSQjM0ZwwGeQDWzRQB8u654XjstT1U2/iGe+1XTIz JIHVtxAZRkN+NdDb+JNb8Rafp1xbn+2zHtjvdKCbtpU/K+Rz6En1zWn8WI9e0OaeXTbS3j02/P76 SCMmRj33n0/wrxzQtZuvDWrQanZsQ6OPl6A898UAfS/g7xxe6xrtzoGr6V/Z+oQQiZUXOCmcd/wr Y8T22uyeVcaT9lnjiUl7K4QESt2OT0xxXm2r6nB4kuNE8Y+Gbjbq6uLaeFlO0AjJ3Edhj9a9atdX s5Db20l5bNeug3RxyBvmA+bH60AclonjnWdRjvrubw7Kun26kIYwTI7ggFQPx/Su2sbo3tjBcmJ4 TKgfy5BhlyOhqMajp6XTWf2mBZxgmLcAefasrRdd1DUNf1TT7rS3tYLQgQzFsiUf55oA6Gis6Cyv Y9ZuLqS/aS0kQCO224CHnJzmtGgAorP1bV4dIjgeaGeUTSiICFNxBPc+1Y0njzS7XxBc6TeiS0MI B86YAI305zQB1NFZ+pa5pmj2iXWoXkdvA/3XfODWTqPj7w9plnZ3Ut4XhvD+6aNc5Hcn2oA6aish vFOhJafan1S2WLaGJLjIB9utcJ4i+JV5qn2rTvA9m+oSxxEy3g4SLjnGepoA6fxJ8RfDnhef7Pf3 mbgcmGJS7L9QOlc6fivcavMLbwv4fu76Q4/eyKVRfr3FL4O8NWy+EX1qC0W/124Us73gx8/93vgV 6DpsTJYQGa3iguDGvmJGOFbHIBoA4A+B9e8VWE8XivUnhjlnWUW9sRgDHK554rudF0PTvD+mx6fp lssFtH0Uc/iSetaNFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBFc20 N5bSW9wgeKRSrKe4NfKfxP8ADNv4a8Xz2enxmG0Ko8KhySPlHGT75r6xryj4tT+GJ7a4tZby3g17 ywisyEsE64z2BB60AeMeDvE02galcQPI8dpeQFJcH7nowA754z7mva/g9oOitoEWrrGsuqrJIk0p csVO444zgcYr51ktEtbuOKZy4GMlORgn/wCsa90+Gt3babq+uHRHe40qKzikRHbbvl2DIGe5bigD d8f+Eb6fxRo/iDSLYyvFKFuUTqRxg4/OvRTe2q3i2bXEQunXesJYbiPXFR6Zdy32mwXM9s1tLIu5 oWOSh9M1lapq/hvTNTkvL2aBdQtYck/8tFQ84oA6Ck3rv2bhuxnHevN7b4paZF4gmtpb1r6C5dfs q2sOTCO+8k+46V0mqWF5D4gS+0y03TXURhmuGk4iAHynbjntQB0teO/FxLez1XStWsrWO4uxcrHN vG9GHQIw6DPHvXZ+DtF8SaXqGpza7qEd2lwVMWxj8uM54I46it620LTrWa5ljtlLXEnmybuct60A efi98Z61eWdvqXg/TW03cNwk+baPUZOBXW+IbPQ7PTYUutHSeI/6PGsMAJjD/KcYHHWr/iHRzrmj yWKXUlq5IZJY+qkdPqKxrnwZcT6jpOpjV51vrNUSd8ZWdQOflzwT60Acn4j+EumwxQ3+gaULq53D fBcXD7Cvqctnj0rsbTT38NaVaDTNDg+03Lxi8SDgLnAZvcDmupooAbGixoFRFQf3VGAKdRRQAUUU UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXgfxdtE8Q+NF03TtMmkv YYlE80aFiQwyOB9RXvlQi0t1uWuRCgnYbTJjkigD431PRL6w1BbG4s54JvKEjKUO4e+Ote8eAvCe hX/gM6Ob4TXM224uDC+2SM8FenPHHWvSH0bTZL9757KFrp08tpSuSV9KfaaXY2Mry2trHC7gBmQY yBwKADTLBNL023sklklWFNoeVtzH6muZ1X4baHrfiC71fUBNM9yioYw5VRgAZ4wegrsaKAOD8OfC zSvDPiFtUtLiZ1ZCvkSgMoyR7Z7V3lFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR RQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFF ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUA FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf//Z