20191211 13452800 1.0 TRUE This dataset is the result of a 2019 park tree study which involved 394 sample plots across 44 Arlington parks. The data are organized by park and include the most common species found at each park, the approximate level of interest or uniqueness of the tree species, an objective classification of the best time to view the trees, and relative presence of select tree categories (oak, elm, ash, maple, conifer, nut, fruit, thorn, and bloom species). The data may be filtered to only sampled parks using the field "Sample2019". Trees per acre were estimated for all 98 parks using a combination of sample plots, tree inventories, and estimated tree cover. FAB Forestry Arlington Parks Parks & Recreation Recreation Tree Data Trees Urban Forestry Arlington, TX Park Tree data from 2019 study. This layer provides an estimate of the number of trees per acre for 98 Arlington parks. Also included are the ten most common species and relative presence of trees in defined categories for 44 parks. Jeremy Priest, Jamie Najar; Arlington Forestry and Beautification /9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCACFAMgDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAR3WNC7sFUdSTgCkjkSaJJYnV43UMrqchgehB7 iuD+JvjK78MWtpaWcCl78OrTuCRGowDgZGW+b17V1nh/TG0Xw/Y6a8ole2iEZcAAMR9AP8+tSpJy cV0IU05OK6GlRRRVFhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHler/E7XLrV Lmw8L+H5bprKUpcSPE0ucEjG1OgOOpNGm/EvxDaalFF4l8N3NvHdyJFbiG3dDu78Ofm6jgV0Vno8 XhW7u9Qs7F1mvnJuCXD725bIBcY/i/Ota40Q6xfabfakBG+nzvLDHEx+YkAKWPt1wPbPcV59Cu60 mtVJdGraX/UxdKqvecv8jF0D4lafrWvf2JNYXljfl3VUmAxhQTz0IYgdMfia7Q/OGVXwemRjKmuY 1fT9M1fV7dp7SAzwy7fMDbZT8yjqMEcA9+lTSaDdQ6pt025+xWM675mhRfMDqAFAJHTPPOc4YHrW 9DERquai78rs/X/gGijOK97Uo6hBe+JtWXSpbZV0+wuI5Z7maI/vmBYhY+Np42A/U+nPY1n2VmdJ ggtYBNcrJM7TzzSDcC25i54GctgYAGM+grQroSGl1CiiimMKKKKACiiigAooooAKKKKACiiigAoo ooAKKKKACiiigAoqGe6htngSViGnk8qMBSdzYJxx7KT+FTEAggjIPagCpb3Njq1sXgeK5hDYJxuA OM/1qxuk88r5Y8rbnfu53Z6Yx+tYvhW2e2sbtXtmt910zKjJt4wvOK3aypSc4KbVmypx5XylCaGD W9Ou7K7tZkgkLQukg2lh0yCO3oRRpmj2ekQpDZI0cSRrGE3EjC9D9eeT3q/VL7eX1NrKGBmaMK0r udqqp6Y/vdD0496006k26lqWVIU3yNtXIXPuTgfqRT6KKYBRRRQAUUUUAFFFFABRRRQAUUUUAFFF FABRRRQAUUUUAFQ3d1HZ2rzy7iqDooySewHuaJ7q3tRmeaOP5Sw3MASB1I9cVG8dpqtoMlZ4GO4F W4yPcdwf1pXV7Ds9zjrC68X69renz6hpVtpWm21x5yZl8yWT924xwcYwx6gV2yvMbh0MQEIUFZN/ LHuMY4/OiaWK2h8yT5UUgDAz1OB/Oi4t4rqPy5lLLnONxH8qSTS0d2TGPKt7ktN2sA2HyScgsOnt ximqwSQQrGwUJkEL8oHTFOBfdhgMc8jt6VQw4kQ7W4IxkGsqwhZNVuJEEzQEBQ7yEgkcHr1+v1pq 3dxqF15Vi3+hhh5kwXbgc5VSepJxyMcfUGrWlWQ0nTVs3uGlWEu3myDHylmYZPTgHH4dqyaU2mtk PoX6KhSWV7jCxobYxhlmEmSzEnjbjpjBznvTGvFaKVreNp3jk8sovHzd+Tgd+taXCxZopkTO8KNJ H5bkAsmc7T6Z70+mIKKKKACiiigAooooAKKKKACiiigAooooAKKKKAMu9s4NZt547u1kh8lyscjA biAAdy4zwfTviqUFnPoXh+SKG3ee9kVizQn+LscnpgEdu1dDRWLoQ9p7VL3rWuae1lycl9OxU0qW WbSrWSeZZpjEvmOoABbHPA6c5qxKzJE7InmOFJVAcbj6ZNcLFa6rF8R7GOyN0+i2cbxzfP8AIu5S VUg4zg9CM13tXCTa1Mk7t6CKSUBZdpI5GelVri8MEV24t5nNvH5mFTPmcE4X1PH6ipbYzm3Q3IQT Y+cJ93PtWT4o1dNH0pZyJnczR4SH77AMCwHtgEfjTlJJXZcIOclGO7Na3ijhhCRKVQktg+pOT+pp ZzMIHNusbTY+QSMVUn3IBIH4VFYXRvtPgujBJB5yB/Lk+8oPTNSW9xDdwLPbypLE2drocg844Pen e4mmnZhbmc26G5WNZsfOI2LKD7EgE/lUtRQ3CTyzxpndBII3z67Vbj8GFS0xMKKKKACiiigAoooo AKKKKACiiigAooooAKKKKACoLZblWn+0OjqZSYdowQmBwffOfwxUkzSLBI0MayShSURm2hmxwCcH H1wacpJUFhg45AOcUAMniaaEosjRkkfMvXGcn/CpDnBx1opA6lioYFh1GeRQBDBPnbBNLCbpVzIk bdPfHUCpyQOpxUMeDdTMbZo2AVfOO396OSMYOcAk9QOScUXFtHciISgkRyLIuDj5h0pATVTZLie1 VRLHBeqE3yJHvVTkFguexxj24NOv9Rs9MtxPe3CQRFgu9zgZPSrKsGUMpyCMgjvRpsOzSvYWjpRR TEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBV1GCe6064gtpjDM6EJICRtPrkc06xt5 LWxgglnaeSNArSsSSx9easUUuVX5gIUuVe6ltxHKGjAJdoyEOfRuh/CnLbwpO86wxrM4AeQKAzAd MnvUlQ3FrBdCMTxh/KkWVMnow6GgehNUM5uB5X2dYmzIPM8xiMJ3IwDk9OOPrU1crceK7u1srWeT T2b52e72qVENuv3n57gkcdTzScknYNlzGn4h8O23iS0jtbu4uYokbftgYDcffIPSrtjbiytltftU twyD70xUvjtnAA/SrQOQCOhqjqOlQalEUdpYXOP3sDbXwO2fTnpScbNyS1KdSbgoN6L9Se2tvs3n fvpZfMkMn7xs7c/wr6D2qWSNJonikG5HUqw9QawW0HUraxktdM1yWJXU/NcwiZgxGMggrj/Gq7eG 9aGmxWUfiq72hQJHkgR3cfxDfwRx05yPWmm7aozu10NWHVbBboaXZsZp4lC7I1ZljxxhnxtBHoTn 2NadUdJsLDTrGOHTrdYYAAAApBOOMnPJPueTV6mh69QooopgFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABVKC/F1IYkRkYqWVmAKkAj0Oe4q4xIUlRk44BOM15pB4B1V7hDObUJuBdi5ORnnt1ri xVTEQnD2Mbrr/wAP0/EG7dD0OGK6j8oSXCyhVIdtmCx9cdqh1iK4n0ye3tFPnzqYRIMfut3G85Iy B1wOfar9QzRSyT27pcNGkbEyRhQRICpABPUYJB49K7LAVdI0a10W2MFsXIYgku2TwAPy4/U1Ylht NRtSk0UVzA38MihlPboasVmPd38UjxW+jkorHa5nRVYc8gDJ/TvS0igMpfByWcDnSLttLuQzeU9s GMYQ5+Vo2Yqx5+9gHpjGMVbaz8Qspll1iFHjX5Yra0GyQ453ByT1x91l/rVme/uksWabT7uJ3TH+ j7ZWRjnoPbjt3qn4Oj1iLRAmsMzy7yY3kJ8wqTn5gen+fSpulJRQ1CPK5X17f1obNq8hhVJiWmVV 3uIyiscckAk/lk4qeiitBBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf//Z