This layer represents the composite count overlap of six polygon source data sets that consider ecosystem structure, function, and composition in order to estimate relative ecological integrity across the High Divide region. We define and estimate ecological integrity by assembling publicly available spatial data that describe “elements of composition, structure, function, and ecological processes” (after Parrish et al. 2003; Wurtzebach and Schultz 2016) as described below.

CDFW BIOS GIS Dataset, Contact: Marcus Beck, Description: Stream management goals for biological integrity may be difficult to achieve in developed landscapes where channel modification and other factors impose constraints on in-stream conditions. To evaluate potential constraints on biological integrity, we developed a statewide landscape model for California that estimates ranges of likely scores for a macroinvertebrate-based index that are typical at a site for the observed level of landscape alteration.

Forest Landscape Integrity - Nature Communications. https://www.nature.com/articles/s41467-020-19493-3#Sec13Many global environmental agendas, including halting biodiversity loss, reversing land degradation, and limiting climate change, depend upon retaining forests with high ecological integrity, yet the scale and degree of forest modification remain poorly quantified and mapped. By integrating data on observed and inferred human pressures and an index of lost connectivity, we generate a globally consistent, continuous index of forest condition as determined by the degree of anthropogenic modification. Globally, only 17.4 million km2 of forest (40.5%) has high landscape-level integrity (mostly found in Canada, Russia, the Amazon, Central Africa, and New Guinea) and only 27% of this area is found in nationally designated protected areas. Of the forest inside protected areas, only 56% has high landscape-level integrity. Ambitious policies that prioritize the retention of forest integrity, especially in the most intact areas, are now urgently needed alongside current efforts aimed at halting deforestation and restoring the integrity of forests globally.

See https://wa-stateparks.maps.arcgis.com/home/item.html?id=be76cf1a59dc40c3b655ca746aee5820 for project report and metadata.

The New Jersey Department of Environmental Protection (NJDEP) Bureau of Freshwater and Biological Monitoring (BFBM) performs monitoring on non-tidal freshwater streams and rivers throughout the state using fish as biological indicators of stream health. This data is used for a wide variety of purposes, including the evaluation of aquatic life use assessment for the federally required NJ Integrated Water Quality Assessment Report and the designation of Category One antidegradation classification based on exceptional ecological significance. BFBM has established fish bioassessment protocols for three different stream types in New Jersey. The Bureau initiated Fish Index of Biotic Integrity (IBI) monitoring in 2000 following the development of the Northern Fish IBI by U.S. EPA Region 2 which was based on the EPA’s Rapid Bioassessment Protocols (RBP; USEPA 1999). This, the longest fish monitoring program in the NJDEP Division of Water Monitoring and Standards (DWMS), monitors resident fish assemblages in wadable streams larger than 4-square miles in drainage area. The Southern Fish IBI was developed by BFBM in 2012 for low gradient streams in the Inner Coastal Plain eco-region of NJ. Lastly, after several years of research and analysis by the Philadelphia Academy of Natural Sciences of Drexel University and BFBM, the Headwaters IBI was completed in 2014. This program is used to monitor small first and second order streams less than 4 square miles in drainage area within the same eco-regions of Northern New Jersey as the Northern Fish IBI. The two northern programs differ not only in the size of stream monitored, but also in the assemblages monitored. The Northern Fish IBI is solely a fish-based index, whereas the Headwaters IBI uses fish, crayfish, and streamside amphibians as bio-indicators.

This dataset represents the Scenic Integrity Objectives for the Tongass National Forest. The SIO layer is derived from a combination of Distance Zones (DZs) and LUDs, as described in the table on page 4-57 of the 2008 Forest Plan titled, “Adopted Scenery Objectives for Each Land Use Designation.”

These two datasets represent a normalized least-cost corridor mosaic (see WHCWG 2010 and McRae and Kavanagh 2011) calculated using (1) temperature gradients and a landscape integrity resistance raster, or (2) temperature gradients only, following the climate gradient linkage-modeling methods outlined in Nuñez (2011), using an adapted version of the Linkage Mapper software (McRae and Kavanagh 2011). This GIS dataset is one of several climate connectivity analyses produced by Tristan Nuñez for a Master's thesis (Nuñez 2011). The dataset was produced in part to assist the Climate Change Subgroup of the Washington Wildlife Habitat Connectivity Working Group (WHCWG). The core areas in the map lie in Washington State and neighboring areas in British Columbia, Idaho, and Oregon.This connectivity analysis should be displayed in conjunction with vector layer of Landscape Integrity Core Areas developed by the WHCWG (WHCWG 2010).

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

From Belote et al. 2022, we used the middle tolerance scenario with a 150 m moving window and reclassified raster based on the mean value (.727). Everything above the mean was considered "suitable" connectivity. The layer was clipped to the analysis area and converted into a polygon. Dreiss et al. (2022) extracted raw data values on connectivity and climate flow for areas that were IDed as climate-informed corridors based on categorical connectivity and climate flow dataset (TNC 2020). The remaining values were rescaled to fall between 0 and 1. A second climate corridor dataset (Carroll et al. 2018) was similarly rescaled. These two datasets were combined and locations in the 80th percentile of the distribution of combined values were analyzed. Higher values in the dataset indicate more optimal climate corridors. From Dreiss et al. 2022, here we took the upper 66% of values from the climate-informed wildlife corridors, as the top 33% and 50% were both insufficient to show data in the region given the dataset's national scale. The layer was clipped to the analysis area and converted into a polygon.These two layers were combined using the Count Overlap tool.

This GIS dataset is part of a suite of wildlife habitat connectivity data produced by the Washington Wildlife Habitat Connectivity Working Group (WHCWG). The WHCWG is a voluntary public-private partnership between state and federal agencies, universities, tribes, and non-governmental organizations. The WHCWG is co-led by the Washington Department of Fish and Wildlife (WDFW) and the Washington Department of Transportation (WSDOT). The statewide analysis quantifies current connectivity patterns for Washington State and adjacent areas in British Columbia, Idaho, Oregon and a small portion of Montana. Available WHCWG raster data include model base layers, resistance, cost-weighted distance, landscape integrity networks, focal species networks, and focal species guild networks. Grid cell size is 100meters x 100meters. Habitat concentration areas, landscape integrity core areas, and linkage maps reside in raster and vector format. Cell values represent normalized least-cost scaled in kilometers. Project background can be found in the report: Washington Wildlife Habitat Connectivity Working Group (WHCWG). 2010. Washington Connected Landscapes Project: Statewide Analysis. Washington Departments of Fish and Wildlife, and Transportation, Olympia, WA. Online linkage: http://www.waconnected.org

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making.

   BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions.


   Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself.


   For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise.


   Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. 


   This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery.


   See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt


   See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

This GIS dataset contains polygons depicting U.S. EPA Superfund Institutional Control boundaries. Institutional controls are non-engineered instruments, such as administrative and legal controls, that help to minimize the potential for exposure to contamination and/or protect the integrity of a response action. ICs typically are designed to work by limiting land and/or resource use or by providing information that helps modify or guide human behavior at a site. Superfund features are managed by regional teams of geospatial professionals and remedial program managers (RPMs), and SEGS harvests regional data on a weekly basis to refresh the national dataset and feature services.

The MassDEP Division of Watershed Management (DWM), Watershed Planning Program’s (WPP) 2018/2020 Integrated List of Waters data layer provides EPA-approved water quality assessment and listing decisions for the 2018/2020 reporting cycle, as required by the Clean Water Act (CWA) under Sections 305(b), 314, and 303(d). The objective of the CWA is to restore and maintain the chemical, physical, and biological integrity of the Nation's waters. As one step toward meeting this goal each state must administer a program to monitor and assess the quality of its surface waters and provide periodic status reports to the U.S. Environmental Protection Agency (EPA).More details...Map service also available.

Land parcel data for all properties in the City of Detroit from the Office of the Assessor. These are the parcel boundaries for the 2024 tax year. The Parcels data set from the City of Detroit Office of the Assessor is updated daily with land parcel data for all properties within the City of Detroit. Records in this data set describe the assessed values, rights, ownership interests, most recent sales data, physical descriptions, and addresses associated with each parcel. Parcels are distinguished from lots or plots of land in that property ownership rights are the fundamental units of division between parcels. The graphic depiction of land parcels represents real property ownership within the City for both privately and publicly owned properties.Data maintenance note: Databases used by the Office of the Assessor for parcels data are checked for updates daily. If an update is detected, the published Parcels dataset is then updated to reflect the most recent data available. Similarly, Zoning data provided by the Buildings, Safety Engineering, and Environmental Department (BSEED) Zoning Division is checked for updates daily. If any updates are available, values in the Zoning field are updated to reflect the most recent zoning data available. The "Data Updated" date for the Parcels dataset reflects the most recent date any data updates were detected and incorporated into the Parcels dataset. The GIS / Land Records Maintenance Division in the Office of the Assessor manages the data integrity of the parcel file.

The Vermont Water Quality Standards (VTWQS) are rules intended to achieve the goals of the Vermont Surface Water Strategy, as well as the objective of the federal Clean Water Act which is to restore and maintain the chemical, physical, and biological integrity of the Nation's water. The classification of waters is in included in the VTWQS. The classification of all waters has been established by a combination of legislative acts and by classification or reclassification decisions issued by the Water Resources Board or Secretary pursuant to 10 V.S.A. � 1253. Those waters reclassified by the Secretary to Class A(1), A(2), or B(1) for any use shall include all waters within the entire watershed of the reclassified waters unless expressly provided otherwise in the rule. All waters above 2,500 feet altitude, National Geodetic Vertical Datum, are designated Class A(1) for all uses, unless specifically designated Class A(2) for use as a public water source. All waters at or below 2,500 feet altitude, National Geodetic Vertical Datum, are designated Class B(2) for all uses, unless specifically designated as Class A(1), A(2), or B(1) for any use.

The seamless, county-wide parcel layer was digitized from official Assessor Parcel (AP) Maps which were originally maintained on mylar sheets and/or maintained as individual Computer Aided Design (CAD) drawing files (e.g., DWG). The CRA office continues to maintain the official AP Maps in CAD drawings and Information Systems Department/Geographic Information Systems (ISD/GIS) staff apply updates from these maps to the seamless parcel base in the County’s Enterprise GIS. The seamless parcel layer is updated and published to the Internet on a monthly basis.The seamless parcel layer was developed from the source data using the general methodology outlined below. The mylar sheets were scanned and saved to standard image file format (e.g., TIFF). The individual scanned maps or CAD drawing files were imported into GIS software and geo-referenced to their corresponding real-world locations using high resolution orthophotography as control. The standard approach was to rescale and rotate the scanned drawing (or CAD file) to match the general location on the orthophotograph. Then, appropriate control points were selected to register and rectify features on the scanned map (or CAD drawing file) to the orthophotography. In the process, features in the scanned map (or CAD drawing file) were transformed to real-world coordinates, and line features were created using “heads-up digitizing” and stored in new GIS feature classes. Recommended industry best practices were followed to minimize root mean square (RMS) error in the transformation of the data, and to ensure the integrity of the overall pattern of each AP map relative to neighboring pages. Where available Coordinate Geometry (COGO) & survey data, tied to global positioning systems (GPS) coordinates, were also referenced and input to improve the fit and absolute location of each page. The vector lines were then assembled into a polygon features, with each polygon being assigned a unique identifier, the Assessor Parcel Number (APN). The APN field in the parcel table was joined to the corresponding APN field in the assessor property characteristics table extracted from the MPTS database to create the final parcel layer. The result is a seamless parcel land base, each parcel polygon coded with a unique APN, assembled from approximately 6,000 individual map page of varying scale and accuracy, but ensuring the correct topology of each feature within the whole (i.e., no gaps or overlaps). The accuracy and quality of the parcels varies depending on the source. See the fields RANK and DESCRIPTION fields below for information on the fit assessment for each source page. These data should be used only for general reference and planning purposes. It is important to note that while these data were generated from authoritative public records, and checked for quality assurance, they do not provide survey-quality spatial accuracy and should NOT be used to interpret the true location of individual property boundary lines. Please contact the Sonoma County CRA and/or a licensed land surveyor before making a business decision that involves official boundary descriptions.

Natural Disaster Detection IoT Market size was valued at USD 1.2 Billion in 2023 and is projected to reach USD 4.06 Billion by 2030, growing at a CAGR of 37.2 % during the forecast period 2024-2030.

Global Natural Disaster Detection IoT Market Drivers

Improved Early Warning Systems: The Internet of Things (IoT) makes it possible to implement sophisticated early warning systems for natural disasters such hurricanes, floods, tsunamis, earthquakes, and wildfires. Sensors placed in disaster-prone locations are able to identify environmental anomalies and precursor signals, sending real-time data to central monitoring systems. This makes it easier to notify authorities and locals in a timely manner, lessening the effects of calamities and maybe saving lives.

Enhanced Surveillance and Forecasting: Internet of Things-capable sensors and surveillance apparatuses furnish constant data gathering and examination capacities, imparting discernment into environmental factors like temperature, humidity, pressure, seismic activity, and meteorological trends. This data is processed using sophisticated analytics and machine learning algorithms to find patterns, trends, and early warning signs of impending disasters. This allows for more accurate forecasting and preparedness planning.

Remote sensing and surveillance of disaster-prone locations are made possible by Internet of Things (IoT) devices outfitted with cameras, drones, and satellite imaging technology. Emergency responders and decision-makers can benefit greatly from the situational awareness that these sensors can provide by monitoring changes in the topography, vegetation, water levels, and integrity of infrastructure. Efforts to assess damage, prepare for emergencies, and conduct catastrophe assessments are improved by real-time imagery and video feeds.

Integration with Geographic Information Systems (GIS): Spatial analysis, mapping, and visualization of disaster-related data are made easier by the integration of IoT data with GIS platforms. Decision-making processes are improved by geographic data overlays, risk maps, and geospatial modeling tools, which help authorities identify high-risk areas, allocate resources wisely, and schedule evacuation routes and shelter places.

Developments in Sensor Technology: The spread of IoT devices for natural disaster detection is driven by ongoing developments in sensor technology, such as downsizing, enhanced sensitivity, and low power consumption. Highly weatherproof and resilient sensors can survive extreme weather conditions, which makes them appropriate for use in dangerous and remote areas that are vulnerable to natural disasters.

Government Initiatives and Regulations: Across the globe, governments and regulatory agencies are investing more money and requiring the use of Internet of Things (IoT)-based technologies for resilience and disaster management. Adoption of IoT technologies to improve catastrophe warning, response, and recovery capacities is encouraged by national disaster preparedness programs, financing initiatives, and regulatory frameworks.

Collaborations between the Public and Private Sectors: In the development of Internet of Things (IoT)-based solutions for natural disaster detection, cooperation between public agencies, private businesses, academic institutions, and non-governmental organizations (NGOs) promotes innovation and knowledge exchange. In order to improve community safety and catastrophe resilience, technological development, pilot projects, and field testing are driven by public-private partnerships (PPPs) and collaborative research activities.

Growing Concern and Awareness of Climate Change: The need for Internet of Things (IoT) solutions for disaster detection and mitigation has increased as a result of growing global awareness of climate change and its effects on the frequency and intensity of natural catastrophes. The necessity for preventive actions to mitigate climate-related hazards is acknowledged by stakeholders from all industries, which motivates investments in IoT infrastructure, research, and innovation.

The Clean Water Act requires three components to water quality standards that set goals for and protect each States' waters. The three components are: (1) designated uses that set goals for each water body (e.g., recreational use), (2) criteria that set the minimum conditions to support the use (e.g., bacterial concentrations below certain concentrations) and (3) an antidegradation policy that maintains high quality waters so they are not allowed to degrade to meet only the minimum standards. The designated uses and criteria set the minimum standards for Tier I. Maryland's antidegradation policy has been promulgated in three regulations within the Code of Maryland Regulations (COMAR): COMAR 26.08.02.04 sets out the policy itself, COMAR 26.08.02.04-1, which is discussed here, provides for implementation of Tier II (high quality waters) of the antidegradation policy, and COMAR 26.08.02.04-2 that describes Tier III (Outstanding National Resource Waters or ONRW), the highest quality waters. No Tier III waters have been designated at this time. Tier II antidegradation implementation has the greatest immediate effect on local government planning functions so the Maryland Department of the Environment (MDE) has prepared this set of Tier II GIS data layers to provide technical assistance to local governments working to complete the Water Resources Element of their comprehensive plans as required by HB 1141. As part of this process, MDE has created this dataset representing the official record of all Maryland Tier II (high quality) stream segments as determined by MDE, the regulatory agency responsible for identification and listing of Maryland's Tier II waters. This dataset consists of a digital geospatial representation of all identified Tier II segments which includes those stream segments promulgated in (COMAR) 26.08.02.04-1, and those additional segments proposed during the current Triennial Review of Maryland Regulations, known as the pending list. Pending segments are Tier II segments awaiting promulgation. This is a vector line file that was developed using the 24,000:1 scale National Hydrography Dataset (NHD) coverage for Maryland, and each identified Tier II stream segment has been linked to the NHD using the unique common identifier (COMID) code. MDE uses Maryland Biological Stream Survey (MBSS) data for designating streams as Tier II. Using all MBSS stations sampled within a stream reach (defined as a section of stream from confluence to confluence), an arithmetic mean of the benthic index of biotic integrity (IBI) and the fish IBI is calculated. Only if the means of both the benthic and fish IBIs are greater than or equal to 4.00 is a stream reach designated as Tier II. As such, Tier II streams represent the best streams in Maryland in terms of water quality, water chemistry, habitat, and biotic assemblages. Tier II stream segments can range in length generally terminating at confluences, impoundment outfalls, and tidal boundaries. However, in planning activities, one should consider the entire upstream watershed to a Tier II stream as any changes to this watershed can potentially have an effect on the water quality of the Tier II stream. It is worth noting that once a stream segment is designated as Tier II, this designation lasts in perpetuity regardless of changes in water quality or local landuse. The publicly maintained list of all Tier II waters and for further information regarding Maryland's High Quality Waters, Tier II, please visit http://mde.maryland.gov/programs/Water/TMDL/Integrated303dReports/Pages/Antidegradation.aspxAcknowledgement of the Maryland Department of the Environment, Science Services Administration as a data source would be appreciated in products developed from these data, and such acknowledgement as is standard for citation and legal practices for data sources is expected by users of this data. Sharing new data layers developed directly from these data would also be appreciated by Maryland Department of the Environment Science Services Administration staff. MDE shall not be held liable for improper or incorrect use of this data. These data are not legal documents and are not to be used as such.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Feature Service Link: https://archive.geodata.md.gov/imap/rest/services/Hydrology/MD_ArchivedWaterQuality/FeatureServer/1

Kenya Protected Areas and WetlandsWetlands are among the most important ecosystems in Kenya. The integrity of the country’s water resources and agricultural productivity is sustained by our wetlands. They are nutrient rich and productive most of the year. During the dry seasons, wetlands are the only places where the local communities are able to access quality pasture and their edges support production of vegetables and other quick maturing crops for household consumption. They also control floods and clear water of pollutants through filtration. Wetlands are therefore a key resource for the achievement of Vision 2030.

The contents of this feature layer provide a visual aid for homes constructed during the period between 1945 to 1960. Data supporting the visual aids list which neighborhood these post World War II homes resides in, the style of the homes, along with its condition and integrity.The Historic Preservation Office works with the community to preserve these homes by enhancing archaeological, prehistoric, and historic resources throughout the City of Tempe. This work includes a wide range of partnerships with local homeowners, neighborhoods, developers/architects, boards/commissions, state and national agencies, as well as volunteer and non-profit preservation groupsContact: Will DukeContact E-Mail: will_duke@tempe.govContact Phone: N/ALink: N/AData Source: SQL Server/ArcGIS ServerData Source Type: GeospatialPreparation Method: N/APublish Frequency: As information changesPublish Method: AutomaticData Dictionary