This guide will teach you everything you need to know to successfully migrate your field workflows to Field Maps.

ArcGIS Pro is a different experience. It introduces a project-based file structure, terminology changes, and brand-new tools and capabilities (which you will very likely love once you get used to them). The courses and resources below will clarify the major differences between ArcMap and ArcGIS Pro and help you conquer the learning curve. Goals Understand key ArcGIS Pro terminology. Import map documents, geoprocessing models, and other ArcMap-created items into ArcGIS Pro. Access tools and functionality through the ArcGIS Pro ribbon-based interface.

Although miniaturized data loggers allow new insights into avian migration, incomplete knowledge of basic patterns persists, especially for nightjars. Using GPS data loggers, this study examined migration ecology of the Eastern whip-poor-will (Antrostomus vociferus), across three migration strategies: flyover, short-stay, and long-stay. We documented migration movements, conducted hotspot analyses, quantified land cover within 1-km and 5-km buffers at used and available locations, and modeled habitat selection during migration. From 2018-2020 we captured breeding whip-poor-wills from three study sites in Massachusetts and programmed GPS tags to collect data during fall and spring migration periods. Across 19 individual males (nine of them with repeated years of data), GPS tags collected 479 locations, where 30% were classified as flyover points, 33% as short-stays, and 37% as long-stay locations. We documented seasonal flexibility in migration duration, routes, and stopover locations among individuals and between years. Analyses identified hotspot clusters in fall and spring migration in the Sierra de Tamaulipas in Mexico. Land cover at used locations differed across location types at the 5-km scale, where closed forest cover increased and crop cover decreased for flyover, short-stay, and long-stay locations, and urban cover was lowest at long-stay locations. Discrete choice modeling indicated that habitat selection by migrating whip-poor-wills differs depending on the scale and migration strategy. For example, at the 5-km scale birds avoided urban cover at long-stay locations and selected closed forest cover at short-stay locations. We suggest that whip-poor-wills may use land cover cues at large spatial scales, like 5-km, to influence rush or stay tactics during migration. Methods From 2018-2020, we captured breeding whip-poor-wills from three study sites in Massachusetts and programmed GPS tags to collect data during fall and spring migration periods. Across 19 individual males (nine of them with repeated years of data), GPS tags collected 479 locations, where 30% were classified as flyover points, 33% as short-stays, and 37% as long-stay locations. Data processing We filtered and retained migration data points when loggers connected to ≥ 4 satellites and points had dilution of precision values < 5 to ensure a 3D fix of the location (Forrest et al. 2022, Bakermans et al. 2022). Using 30-m USGS DEM (digital elevation model; http://ned.usgs.gov) data, we generated the altitude of each point by converting the GPS tags’ altitude to altitude above sea level and then subtracted the local elevation (from the DEM) from the bird’s altitude (A. Korpach, pers. communication). Next, we classified migration points based on altitude and number of points at a single location as either flyover, short-stay, or long-stay. Long-stays were locations with ≥ 2 GPS points within the same vicinity (i.e., < 10 km). Short-stay and flyovers consisted of one GPS point at a single location. We differentiated short-stay versus flyover points by altitude based on the altitudes of birds at long-stay locations (mean = 17 m, range = 121 m). Short-stays were locations with elevations < 100 m (mean = 15 m), and flyover locations had an altitude ≥ 100 m above the ground (mean = 800 m). Hotspot Analyses To identify areas of high or low use during migration, we ran an optimized hotspot analysis in ArcGIS 10.8.2 to identify statistically significant spatial clusters of high (hotspot) and low values (coldspot) of migration locations using the Getis-Ord Gi* statistic (Sussman et al. 2019). This tool can “aggregate data, identify an appropriate scale of analysis, and correct for both multiple testing and spatial dependence” (ESRI 2021). Land cover classification We used ArcGIS and quantified land cover types from 2019 data using the 100-m Copernicus Global Land Service layer (Buchhorn et al. 2020). Land cover types were classified as (a) closed forest, (b) open forest, (c) shrubland, (d) herbaceous vegetation (hereafter, grassland), (e) herbaceous wetland, (f) cropland, (g) bare, (h) fresh- or saltwater, and (i) developed land (Buchhorn et al. 2020). Using the geoprocessing features of ArcMap, we quantified land cover at 5-km and 1-km circle at an actual migration location (i.e., used) and random locations (i.e., available). Habitat selection We used discrete choice modeling to determine habitat selection of Eastern whip-poor-will during migration. Discrete choice models examine the probability that an individual chooses a location based on a choice set of alternative available locations (Cooper and Millspaugh 1999). Choice sets included one used location based on the GPS fix and ten available locations. We constructed separate models for each type of migration point (i.e., flyover, short-stay, and long-stay) and spatial scale (i.e., 1 km and 5 km) with individual as a random effect. We used package jagsUI (Kellner 2021) with the software JAGS 4.3.1 (Plummer 2003).

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

The Channel Migration Potential (CHAMP) layer contains stream networks of Western Washington (and much of Western Oregon) with associated data and information important for assessing channel migration activity. It also features information on channel characteristics such as stream flow and physical dimensions. This data layer’s main feature is a classification of channel migration potential based on channel confinement and erosion potential. The layer was derived from existing statewide geospatial datasets and classified according to channel migration measurements by the High Resolution Change Detection (HRCD) project for the Puget Sound Region (Washington Department of Fish and Wildlife, 2014). While the layer identifies the potential for channel migration, it does not predict channel migration rates. Thus, this data layer should be used to screen and prioritize stream reaches for further channel migration evaluation. The tool helps plan and prioritize floodplain management actions such as Channel Migration Zone mapping, erosion risk reduction, and floodplain restoration. The background, use, and development of the CHAMP layer are fully described in Ecology Publication 15-06-003 (full report citation and URL below). That report also describes visual assessment techniques that should be used along with the CHAMP layer to assess channel migration potential. Legg, N.T. and Olson, P.L., 2015, Screening Tools for Identifying Migrating Stream Channels in Western Washington: Geospatial Data Layers and Visual Assessments: Washington State Department of Ecology Publication 15-06-003, 40 p. https://fortress.wa.gov/ecy/publications/SummaryPages/1506003.htmlThe tool developers would like to thank the following people for their contribution to this work: • Brian D. Collins (University of Washington) • Jerry Franklin (Washington Department of Ecology) • Christina Kellum (Washington Department of Ecology) • Matt Muller (Washington Department of Fish and Wildlife) • Hugh Shipman (Washington Department of Ecology) • Terry Swanson (Washington Department of Ecology) This project has been funded wholly or in part by the United States Environmental Protection Agency under Puget Sound Ecosystem Restoration and Protection Cooperative Agreement Grant PC-00J27601 with Washington Department of Ecology. The contents of this document do not necessarily reflect the views and policies of the Environmental Protection Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.Generally, this data layer should be used to screen and prioritize stream reaches for further channel migration evaluation. The data resolution does not allow one to predict channel migration. The classification identifies stream segments for further examination, and those that likely require limited attention or analysis. The potential uncertainty involved in the classification approach is a reason for the visual assessment techniques (described below in Ecology Publication 15-06-003) being described along with the CHAMP data layer.

This dataset is used for status assessment, tidal habitat conservation, restoration, and planning for coastal Maine. This data represents low-lying areas of the non-tidal landscape adjacent to current tidal wetlands that could become marsh migration space as sea levels rise. Each marsh migration scenario represents the extent to which highest astronomical tide intersects undeveloped lands if sea level is increased by 6.1 feet. Predictions for the amount of sea level rise in the next 50-100 years vary, but the fact that sea level is rising is well documented (https://www.maine.gov/dacf/mgs/hazards/slr_ticker/index.html). Tidal marshes are ecologically and economically significant natural systems. Planning for their continued functional existence given various sea level rise scenarios is important for sustaining biodiversity and maintaining ecosystem services. Identifying these areas creates the opportunity for government agencies, municipalities, private conservation organizations, and land managers to plan for compatible uses of the lands and avoid impacts to future tidal marsh or buffers to that marsh space. These data can be paired with similarly created data that provides for scenarios with 0, 1.2, 1.6, 3.9, 6.1, 8.8, and 10.9 foot increases in sea level. Together, these datasets provide frames of reference for incremental increases of predicted sea level rise, to better serve planning purposes at different time frames.

The project Exploring the experiences of highly-skilled refugee women in the UK: An intersectional approach' (ESRI) brought together researchers in Applied Linguistics and Intercultural Communication and Education, who worked with a grassroots women’s group in the North of England. The overarching aim of the ESRI project was to conduct empirical research on and to raise awareness about the barriers that highly-skilled refugee women i.e. individuals with professional qualifications and/or professional experience, face throughout their displacement trajectories. Although a small body of research has explored the barriers to employment faced by highly-skilled refugees in Europe, little research and policy attention has been given to the specific experiences of women. In their journeys to appropriate qualified (re) employment, refugee women are comparatively more disadvantaged than men as, for example, they are often the main caregivers in their families. Our work addressed this gap, and we provided new insights including the challenges they encounter and the support they need from employers and policy makers. We drew data from nine qualitative interviews and three interactive workshops with over 20 highly-skilled refugee women to examine the barriers that these women face throughout their displacement trajectories. Our work also provided important methodological contributions that will be valuable to other researchers who engaged with vulnerable and displaced groups. 'Good practice' in research with vulnerable multilinguals was a core concern for our team and, beyond the core project findings from the interviews, we have collected a set of resources that we are using to train other researchers who want to work with vulnerable groups.

The ZIP file consist of GIS files and an Access database with information about the excavations, findings and other metadata about the archaeological survey.

The project leads for the collection of these data were Shelly Blair (CDFW) and Jerrod Merrell (University of Nevada Reno). Mule deer (52 adult females) from the Pacific herd were captured and equipped with store-onboard GPS collars (Vectronic Plus Vertex Survey Iridium), transmitting data from 2015-2020. Pacific mule deer are found on the western slope of the Sierra Nevada in eastern California and exhibit largely traditional seasonal migration strategies. This population migrates from a multitude of lower elevation areas in the foothills of El Dorado National Forest in winter westward into higher elevation summer ranges. Migrants vary in their movements from shorter (6 km) to longer (41 km) distances.GPS locations were fixed between 1-13 hour intervals in the dataset. To improve the quality of the dataset, the GPS data were filtered prior to analysis to remove locations which were fixed in 2D space and visually assessed as a bad fix by the analyst.The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification and prioritization of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 43 migrating deer, including 149 migration sequences, _location, date, time, and average _location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 7.79 days and 26.72 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. Corridors and stopovers were best visualized using a fixed motion variance of 500 per sequence. Winter range was processed with a fixed motion variance of 1000. All products were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Winter range analyses were based on data from 32 individual deer and 54 wintering sequences. Winter range designations for this herd may expand with a la

Conservation of migratory birds requires improved understanding of the distribution of and threats to their migratory habitats and pathways. Wind energy development poses a potential threat, which may be reduced if facilities avoid or mitigate impacts in migration concentration areas. However, a current lack of information on the distribution of migratory concentration areas in the western U.S. impedes proactive planning. The Wyoming Natural Diversity Database (WYNDD) and The Nature Conservancy (TNC) developed deductive models of migratory bird concentration areas. Models were based on a synthesis of existing literature and expert knowledge concerning bird migration behavior and ecology, represented through GIS datasets, and validated using expert ratings and known occurrences. Our results were migration maps for four functional groups: raptors, wetland birds, riparian birds, and sparse grassland birds. Key factors included in migration models differed among the four groups, but included streams, topography, wind patterns, wetland size, forage availability, flyway location, proximity to streams, and vegetation type and structure. Experts rated all models as good or very good, and there was significant agreement between species occurrence data and the migration models for all groups except raptors. Our maps provide data to companies and agencies planning Wyoming wind developments. Our approach could be replicated elsewhere to fill critical data gaps and better inform conservation priorities and wind development planning.

The project leads for the collection of these data were David Casady (CDFW) and Heiko Wittmer (Victoria University of Wellington). Black-tailed deer (65 adult females) from the Mendocino/ Clear Lake/ Alder Springs herd complex (herafter: Mendocino herd) were captured and equipped with store-onboard GPS collars (Lotek Wireless models 3300 and 4400 M, Telonics model TGW-3500), transmitting data from 2004-2013. An additional 24 female black-tailed deer were captured from the Mendocino herd and fit with Lotek Iridiumtrack M GPS collars, transmitting data from 2017-2021. The project lead for this overlapping dataset was Josh Bush (CDFW). Mendocino mule deer exhibit variable movement patterns and strategies. This population includes traditional seasonal migrants, full-time residents, and multi-range migrants (i.e., deer with long-term spring and/or fall stopovers). Full-time residents were excluded from the analysis, but individual deer exhibiting any type of directed movement between high-use ranges were considered a migrant and included. Based on this analysis, the portion of the population that migrates between seasonal ranges does so from a multitude of lower elevation areas within the mountainous Mendocino National Forest in winter to higher elevation summer ranges. Migrants vary in their movements from shorter (2 km) to longer (25 km) distances. While this analysis clearly demonstrates certain movement corridor areas with higher concentrations of migrating deer, with a larger dataset, local biologists predict high-use winter ranges throughout valley bottoms in Mendocino National Forest, and possible high fidelity to summer range sites for individual deer in the area. Numerous black-tailed deer papers have been published as a result of this data collection effort (Casady and Allen 2013; Forrester et al. 2015; Lounsberry et al. 2015; Marescot et al. 2015; Bose et al. 2017; Bose et al. 2018; Forrester and Wittmer 2019).GPS locations were fixed between 1-13 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification and prioritization of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 50 migrating deer, including 125 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The dataset was divided into four overlapping subgroups (i.e., north, central, south, east) and analyzed separately, but visualized together as a final product. The average migration time and average migration distance for deer was 7.43 days and 11.22 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. Corridors were best visualized using a 200 m buffer around the lines due to large Brownian motion variance parameters per sequence. Winter ranges and stopovers were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours and a fixed motion variance of 400. Winter range analyses were based on data from 45 individual deer and 65 wintering sequences. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell, with greater than or equal to 1 deer, greater than or equal to 3 deer (10% of the subgroup sample), and greater than or equal to 5 deer (20% of the subgroup sample) representing migration corridors, moderate use corridors, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.

This dataset is used for status assessment, habitat conservation, and planning for coastal areas of Maine. This subset of data published by the Department of Agriculture, Conservation and Forestry represents low lying areas of the non-tidal landscape that are adjacent to tidal estuaries that could be inundated at highest annual tide if sea level is increased by 3.3 feet. Tidal marshes are ecologically and economically significant natural systems. Planning for their continued functional existence given various sea level rise scenarios is beneficial to both society and wildlife. Predictions for the amount of sea level rise in the next 50 to 100 years vary, but the fact that sea level is rising has been documented. This dataset is intended to be used to identify areas of the landscape where existing tidal marshes could migrate or expand to given a 3.3 foot increase in sea level. Identifying these areas creates the opportunity for government agencies, towns, private conservation organizations, and land managers to plan for compatible uses of the lands before they become inundated. This data can be paired with similarly created data that provides for scenarios with 1, 2, and 6 foot increases in sea level. Together, these datasets provide frames of reference for incremental increases of predicted sea level rise, to better serve planning purposes at different time frames.

To assess site resilience, we divided the coast into 1,232 individual sites centered around each tidal marsh or complex of tidal habitats. For each site, we estimated the amount of migration space available under four sea-level rise scenarios and we identified the amount of buffer area surrounding the whole tidal complex. We then examined the physical properties and condition characteristics of the site and its features using newly developed analyses as well as previously published and peer-reviewed datasets.Sites vary widely in the amount and suitability of migration space they provide. This is determined by the physical structure of the site and the intactness of processes that facilitate migration. A marsh hemmed in by rocky cliffs will eventually convert to open water, whereas a marsh bordered by low lying wetlands with ample migration space and a sufficient sediment supply will have the option of moving inland. As existing tidal marshes degrade or disappear, the amount of available high-quality migration space becomes an indicator of a site’s potential to support estuarine habitats in the future. The size and shape of a site’s migration space is dependent on the elevation, slope, and substrate of the adjacent land. The condition of the migration space also varies substantially among sites. For some tidal complexes, the migration space contains roads, houses, and other forms of hardened structures that resist conversion to tidal habitats, while the migration space of other complexes consists of intact and connected freshwater wetlands that could convert to tidal habitats.Our aim was to characterize each site’s migration space but not predict its future composition. Towards this end, we measured characteristics of the migration space related to its size, shape, volume, and condition, and we evaluated the options available to the tidal complex to rearrange and adjust to sea level rise. In the future, the area will likely support some combination of salt marsh, brackish marsh and tidal flat, but predictions concerning the abundance and spatial arrangement of the migration space’s future habitats are notoriously difficult to make because nature’s transitions are often non-linear and facilitated by pulses of disturbance and internal competition. For instance, in response to a 1.4 mm increase in the rate of SLR, the landward migration of low marsh cordgrass in some New York marshes appears to be displacing high marsh (Donnelly & Bertness 2001). Thus, our assumption was simply that a tidal complex with a large amount of high quality and heterogeneous migration space will have more options for adaptation, and will be more resilient, than a tidal complex with a small amount of degraded and homogenous migration space.To delineate migration space for the full project area, we requested the latest SLR Viewer (Marcy et al. 2011) marsh migration data, with no accretion rate, for all the NOAA geographic units within the project area, from NOAA (N. Herold, pers. comm., 2018). Specifically, we obtained data for the following states in the project area: Virginia, North Carolina, South Carolina, Georgia, and Florida. As accretion is very location-dependent, we chose not to use one of the three SLR Viewer accretion rates because they were flat rates applied across each geographic unit. For each geography, we combined four SLR scenarios (1.5’, 3’, 4’, and 6.5’) with the baseline scenario to identify pixels that changed from baseline. We only selected cells that transitioned to tidal habitats (unconsolidated shoreline, salt marsh, and transitional / brackish marsh) and not to open water or upland habitat. We combined the results from each of the geographies and projected to NAD83 Albers. The resultant migration space was then resampled to a 30-m grid and snapped to the NOAA 2010 C-CAP land cover grid (NOAA, 2017). The tidal complex grid and the migration space grid were combined to ensure that there were no overlapping pixels. While developed areas were not allowed to be future marsh in NOAA’s SLR Viewer marsh migration model, we still removed all roads and development, as represented in the original 30-m NOAA 2010 C-CAP land cover grid, from the migration space. We took this step as differences in spatial resolution between the underlying elevation and land cover datasets could occasionally result in small amounts of development in our resampled migration space. The remaining migration space was then spatially grouped into contiguous regions using an eight-neighbor rule that defined connected cells as those immediately to the right, left, above, or diagonal to each other. The region-grouped grid was converted to a polygon, and the SLR scenario represented by each migration space footprint was assigned to each polygon. Finally, the migration space scenario polygons that intersected any of the tidal complexes were selected. Because a single migration space polygon could be adjacent to and accessible to more than one tidal complex unit, each migration space polygon was linked to their respective tidal complex units with a unique ID by restructuring and aggregating the output from a one-to-many spatial join in ArcGIS. This linkage enabled the calculation of attributes for each tidal complex such as total migration space acreage, total number of migration space units, and the percent of the tidal complex perimeter that was immediately adjacent to migration space. Similar attributes were calculated for each migration space unit including total tidal complex acreage and number of tidal complex units.This dataset shows additional migration space units in the project area for the 3.0-foot sea level rise scenario. Additional migration space units are migration space units that did not spatially intersect current tidal marshes or were spatially disjunct from the migration space of current tidal marshes. Because additional migration space units were not directly associated with a tidal complex, these units were NOT used in the calculation of a tidal complex’s resilience score. The spatial separation could be due to roads, waterbodies, waterways, oil and gas fields, etc. Depending on local factors and context, the degree to which these features will prevent marshes from accessing the additional migration space areas in the future is unknown and likely varies by site.There were thousands of small and disconnected additional migration space areas, often individual pixels, typically found in urban settings, remote upstream riverine areas, or far from any migration space units or tidal marshes. We did not consider these isolated occurrences as additional migration space because they are unlikely to be important future marsh areas. We identified isolated migration space areas using the following approach. First, for unconfirmed additional migration space areas, an iterative analysis of the Euclidean distance from current tidal marshes and their migration space areas, including confirmed additional migration space, was performed. Next, pixels that did not meet the distance thresholds in the first step but were within 60 meters of a NHDPlus v2 (USEPA & USGS, 2012) streamline were retained as additional migration space. Any remaining pixels less than or equal to two acres in size were then removed from the additional migration space. Finally, visual inspection was used to remove isolated migration space areas that were not identified through the previous steps. We assigned resilience scores to the additional migration space areas using several approaches. First, we spatially allocated resilience scores based on Euclidean distance from tidal marshes or migration space units. While this approach was a good starting point, there were migration space areas whose score assignments had to be done manually or by taking the highest of two equidistant nearby scores. The manual assignment included straightforward cases, but often it was unclear how marshes might move into a migration space area (e.g., will marsh travel through waterways to nearby migration space areas; will marsh use all migration space areas along a waterway or waterbody or only on the same side as the current marsh?). For sites with unclear relationships to current marshes and their migration space, the highest resilience score in the general geographic area of the additional migration space was assigned. Consequently, please interpret the scores of the additional migration space with caution and use local expertise and knowledge as you see fit. REFERENCESChaffee, C, Coastal policy analyst for the R.I. Coastal Resources Management Council. personal communication. April 4, 2017.Donnelly, J.P, & Bertness, M.D. 2001. Rapid shoreward encroachment of salt marsh cordgrass in response to accelerated sea-level rise. PNAS 98(25) www.pnas.org/cgi/doi/10.1073/pnas.251209298Herold, N. 2018. NOAA Sea Level Rise (SLR) Viewer marsh migration data (10-m), with no accretion rate, for all SLR scenarios from 0.5-ft. to 10.0-ft. for VA, NC, SC, GA, and FL. Personal communication Jan. 24, 2018. Lerner, J.A., Curson, D.R., Whitbeck, M., & Meyers, E.J., Blackwater 2100: A strategy for salt marsh persistence in an era of climate change. 2013. The Conservation Fund (Arlington, VA) and Audubon MD-DC (Baltimore, MD).Lucey, K. NH Coastal Program. Personal Communication. April 4, 2017.Maine Natural Areas Program. 2016. Coastal Resiliency Datasets, Schlawin, J and Puryear, K., project leads. http://www.maine.gov/dacf/mnap/assistance/coastal_resiliency.htmlMarcy, D., Herold, N., Waters, K., Brooks, W., Hadley, B., Pendleton, M., Schmid, K., Sutherland, M., Dragonov, K., McCombs, J., Ryan, S. 2011. New Mapping Tool and Techniques For Visualizing Sea Level Rise And Coastal Flooding Impacts. National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center. Originally published in the Proceedings of the 2011 Solutions to Coastal Disasters Conference, American Society of Civil Engineers

Indicator 10.7.2Proportion of countries with migration policies that facilitate orderly, safe, regular and responsible migration and mobility of people.Methodology:The indicator includes a total of 30 sub-categories, under 6 questions/domains. All sub-categories, except for those under domain 1, have dichotomous “Yes/No” answers, coded “1” for “Yes” and “0” for “No”. For the sub-categories under domain 1, there are three possible answers: “Yes, regardless of immigration status”, coded “1”; “Yes, only for those with legal immigration status”, coded “0.5”; and “No” coded “0”.Data Source:Ministry of Labor.

Resilience—the keen ability of people to adapt to changing physical environments—is essential in today's world of unexpected changes.Resilient Communities across Geographies edited by Sheila Lakshmi Steinberg and Steven J. Steinberg focuses on how applying GIS to environmental and socio-economic challenges for analysis and planning helps make communities more resilient.A hybrid of theory and action, Resilient Communities across Geographies uses an interdisciplinary approach to explore resilience studied by experts in geography, social sciences, planning, landscape architecture, urban and rural sociology, economics, migration, community development, meteorology, oceanography, and other fields. Geographies covered include urban and rural, coastal and mountainous, indigenous areas in the United State and Australia, and more. Geographical Information Systems (GIS) is the unifying tool that helped researchers understand resilience.This book shows how GIS:integrates quantitative, qualitative, and spatial data to produce a holistic view of a need for resilience.serves as a valuable tool to capture and integrate knowledge of local people, places, and resources.allows us to visualize data clearly as portrayed in a real-time map or spatial dashboard, thus leading to opportunities to make decisions.lets us see patterns and communicate what the data means.helps us see what resources they have and where they are located.provides a big vision for action by layering valuable pieces of information together to see where gaps are located, where action is needed, or how policies can be instituted to manage and improve community resilience.Resilience is not only an ideal; it is something that people and communities can actively work to achieve through intelligent planning and assessment. The stories shared by the contributing authors in Resilient Communities across Geographies help readers to develop an expanded sense of the power of GIS to address the difficult problems we collectively face in an ever-changing world.AUDIENCEProfessional and scholarly. Higher education.AUTHOR BIOSSheila Lakshmi Steinberg is a professor of Social and Environmental Sciences at Brandman University and Chair of the GIS Committee, where she leads the university to incorporate GIS across the curriculum. Her research interests include interdisciplinary research methods, culture, community, environmental sociology, geospatial approaches, ethnicity, health policy, and teaching pedagogy.Steven J. Steinberg is the Geographic Information Officer for the County of Los Angeles, California. Throughout his career, he has taught GIS as a professor of geospatial sciences for the California State University and, since 2011, has worked as a geospatial scientist in the public sector, applying GIS across a wide range of both environmental and human contexts.Pub Date: Print: 11/24/2020 Digital: 10/27/2020ISBN: Print: 9781589484818 Digital: 9781589484825Price: Print: $49.99 USD Digital: $49.99 USDPages: 350 Trim: 7.5 x 9.25 in.Table of ContentsPrefaceChapter 1. Conceptualizing spatial resilience Dr. Sheila Steinberg and Dr Steven J. SteinbergChapter 2. Resilience in coastal regions: the case of Georgia, USAChapter 3. Building resilient regions: Spatial analysis as a tool for ecosystem-based climate adaptationChapter 4. The mouth of the Columbia River: USACE, GIS and resilience in a dynamic coastal systemChapter 5. Urban resilience: Neighborhood complexity and the importance of social connectivityChapter 6. Mapping Indigenous LAChapter 7. Indigenous Martu knowledge: Mapping place through song and storyChapter 8. Developing resiliency through place-based inquiry in CanadaChapter 9. Engaging Youth in Spatial Modes of Thought toward Social and Environmental ResilienceChapter 10. Health, Place, and Space: Public Participation GIS for Rural Community PowerChapter 11. Best Practices for Using Local KnowledgeContributorsIndex

To assess site resilience, we divided the coast into 1,232 individual sites centered around each tidal marsh or complex of tidal habitats. For each site, we estimated the amount of migration space available under four sea-level rise scenarios and we identified the amount of buffer area surrounding the whole tidal complex. We then examined the physical properties and condition characteristics of the site and its features using newly developed analyses as well as previously published and peer-reviewed datasets.Sites vary widely in the amount and suitability of migration space they provide. This is determined by the physical structure of the site and the intactness of processes that facilitate migration. A marsh hemmed in by rocky cliffs will eventually convert to open water, whereas a marsh bordered by low lying wetlands with ample migration space and a sufficient sediment supply will have the option of moving inland. As existing tidal marshes degrade or disappear, the amount of available high-quality migration space becomes an indicator of a site’s potential to support estuarine habitats in the future. The size and shape of a site’s migration space is dependent on the elevation, slope, and substrate of the adjacent land. The condition of the migration space also varies substantially among sites. For some tidal complexes, the migration space contains roads, houses, and other forms of hardened structures that resist conversion to tidal habitats, while the migration space of other complexes consists of intact and connected freshwater wetlands that could convert to tidal habitats.Our aim was to characterize each site’s migration space but not predict its future composition. Towards this end, we measured characteristics of the migration space related to its size, shape, volume, and condition, and we evaluated the options available to the tidal complex to rearrange and adjust to sea level rise. In the future, the area will likely support some combination of salt marsh, brackish marsh and tidal flat, but predictions concerning the abundance and spatial arrangement of the migration space’s future habitats are notoriously difficult to make because nature’s transitions are often non-linear and facilitated by pulses of disturbance and internal competition. For instance, in response to a 1.4 mm increase in the rate of SLR, the landward migration of low marsh cordgrass in some New York marshes appears to be displacing high marsh (Donnelly & Bertness 2001). Thus, our assumption was simply that a tidal complex with a large amount of high quality and heterogeneous migration space will have more options for adaptation, and will be more resilient, than a tidal complex with a small amount of degraded and homogenous migration space.To delineate migration space for the full project area, we requested the latest SLR Viewer (Marcy et al. 2011) marsh migration data, with no accretion rate, for all the NOAA geographic units within the project area, from NOAA (N. Herold, pers. comm., 2018). Specifically, we obtained data for the following states in the project area: Virginia, North Carolina, South Carolina, Georgia, and Florida. As accretion is very location-dependent, we chose not to use one of the three SLR Viewer accretion rates because they were flat rates applied across each geographic unit. For each geography, we combined four SLR scenarios (1.5’, 3’, 4’, and 6.5’) with the baseline scenario to identify pixels that changed from baseline. We only selected cells that transitioned to tidal habitats (unconsolidated shoreline, salt marsh, and transitional / brackish marsh) and not to open water or upland habitat. We combined the results from each of the geographies and projected to NAD83 Albers. The resultant migration space was then resampled to a 30-m grid and snapped to the NOAA 2010 C-CAP land cover grid (NOAA, 2017). The tidal complex grid and the migration space grid were combined to ensure that there were no overlapping pixels. While developed areas were not allowed to be future marsh in NOAA’s SLR Viewer marsh migration model, we still removed all roads and development, as represented in the original 30-m NOAA 2010 C-CAP land cover grid, from the migration space. We took this step as differences in spatial resolution between the underlying elevation and land cover datasets could occasionally result in small amounts of development in our resampled migration space. The remaining migration space was then spatially grouped into contiguous regions using an eight-neighbor rule that defined connected cells as those immediately to the right, left, above, or diagonal to each other. The region-grouped grid was converted to a polygon, and the SLR scenario represented by each migration space footprint was assigned to each polygon. Finally, the migration space scenario polygons that intersected any of the tidal complexes were selected. Because a single migration space polygon could be adjacent to and accessible to more than one tidal complex unit, each migration space polygon was linked to their respective tidal complex units with a unique ID by restructuring and aggregating the output from a one-to-many spatial join in ArcGIS. This linkage enabled the calculation of attributes for each tidal complex such as total migration space acreage, total number of migration space units, and the percent of the tidal complex perimeter that was immediately adjacent to migration space. Similar attributes were calculated for each migration space unit including total tidal complex acreage and number of tidal complex units.This dataset shows additional migration space units in the project area for the 6.5-foot sea level rise scenario. Additional migration space units are migration space units that did not spatially intersect current tidal marshes or were spatially disjunct from the migration space of current tidal marshes. Because additional migration space units were not directly associated with a tidal complex, these units were NOT used in the calculation of a tidal complex’s resilience score. The spatial separation could be due to roads, waterbodies, waterways, oil and gas fields, etc. Depending on local factors and context, the degree to which these features will prevent marshes from accessing the additional migration space areas in the future is unknown and likely varies by site.There were thousands of small and disconnected additional migration space areas, often individual pixels, typically found in urban settings, remote upstream riverine areas, or far from any migration space units or tidal marshes. We did not consider these isolated occurrences as additional migration space because they are unlikely to be important future marsh areas. We identified isolated migration space areas using the following approach. First, for unconfirmed additional migration space areas, an iterative analysis of the Euclidean distance from current tidal marshes and their migration space areas, including confirmed additional migration space, was performed. Next, pixels that did not meet the distance thresholds in the first step but were within 60 meters of a NHDPlus v2 (USEPA & USGS, 2012) streamline were retained as additional migration space. Any remaining pixels less than or equal to two acres in size were then removed from the additional migration space. Finally, visual inspection was used to remove isolated migration space areas that were not identified through the previous steps. We assigned resilience scores to the additional migration space areas using several approaches. First, we spatially allocated resilience scores based on Euclidean distance from tidal marshes or migration space units. While this approach was a good starting point, there were migration space areas whose score assignments had to be done manually or by taking the highest of two equidistant nearby scores. The manual assignment included straightforward cases, but often it was unclear how marshes might move into a migration space area (e.g., will marsh travel through waterways to nearby migration space areas; will marsh use all migration space areas along a waterway or waterbody or only on the same side as the current marsh?). For sites with unclear relationships to current marshes and their migration space, the highest resilience score in the general geographic area of the additional migration space was assigned. Consequently, please interpret the scores of the additional migration space with caution and use local expertise and knowledge as you see fit. REFERENCESChaffee, C, Coastal policy analyst for the R.I. Coastal Resources Management Council. personal communication. April 4, 2017.Donnelly, J.P, & Bertness, M.D. 2001. Rapid shoreward encroachment of salt marsh cordgrass in response to accelerated sea-level rise. PNAS 98(25) www.pnas.org/cgi/doi/10.1073/pnas.251209298Herold, N. 2018. NOAA Sea Level Rise (SLR) Viewer marsh migration data (10-m), with no accretion rate, for all SLR scenarios from 0.5-ft. to 10.0-ft. for VA, NC, SC, GA, and FL. Personal communication Jan. 24, 2018. Lerner, J.A., Curson, D.R., Whitbeck, M., & Meyers, E.J., Blackwater 2100: A strategy for salt marsh persistence in an era of climate change. 2013. The Conservation Fund (Arlington, VA) and Audubon MD-DC (Baltimore, MD).Lucey, K. NH Coastal Program. Personal Communication. April 4, 2017.Maine Natural Areas Program. 2016. Coastal Resiliency Datasets, Schlawin, J and Puryear, K., project leads. http://www.maine.gov/dacf/mnap/assistance/coastal_resiliency.htmlMarcy, D., Herold, N., Waters, K., Brooks, W., Hadley, B., Pendleton, M., Schmid, K., Sutherland, M., Dragonov, K., McCombs, J., Ryan, S. 2011. New Mapping Tool and Techniques For Visualizing Sea Level Rise And Coastal Flooding Impacts. National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center. Originally published in the Proceedings of the 2011 Solutions to Coastal Disasters Conference, American Society of Civil Engineers

Washington's State Wildlife Action Plan (SWAP, 2015) is a comprehensive plan for conserving the state's fish and wildlife and the natural habitats on which they depend. It is part of a nationwide effort by all 50 states and five U.S. territories to develop conservation action plans and participate in the State and Tribal Wildlife Grants (SWG) Program. These data layers represent both the observed species range, as well as, the modeled potential species range for Species of Greatest Conservation Need (SGCN), as identified in Washington’s State Wildlife Action Plan, 2015. Species range is defined as the geographic area, in which a species regularly occurs within Washington, including areas used for breeding as well as important distinct foraging, wintering, or migration areas, where appropriate. Range does not include accidental, infrequent, or peripheral areas that are disconnected from the regularly occurring area or wintering or migration areas that are generally broad and nonspecific. SWAP SGCN species ranges (observed and potential) are spatially represented, using USGS watershed boundaries (USGS hydrologic units - HUCS) at various scales. Potential suitable habitat distribution data for each of the SCGN species was derived from “Ecological Systems” data. Ecological Systems were developed by NatureServe to provide a mid-scale ecological classification, for uplands and wetlands, useful for conservation and environmental planning. The Washington State SWAP data is organized as a collection of individual species ranges (observed and potential range polygons), as well as, potential habitat distribution raster files - that have been compressed (into a ZIP files) for download. No map services exist for these data at this time.