LandRise (LightPath)

data identification and collection steps are sometimes considered the most difficult and demanding aspects of GIS projects.

Data must be identified and collected for all relevant spatial and non-spatial data for a parcel level of interest. Spatial data might include the boundaries of the parcels, which can often be obtained from one entity in the local government and, in some cases, driven from recent satellite imagery. Non-spatial data could include property characteristics like the age of structures, building materials, square footage, and so forth, often found in property tax records. Let's break down the types of data you might need to collect for a comprehensive property valuation:

Property assessment: The methods vary by jurisdiction, but three primary methods are commonly used. First, the market approach values a property based on what similar properties have sold for in the same area, considering the property's location, size, condition, and features and the prices at which similar properties have recently sold. Second, the cost approach, often used for unique or special-purpose properties or new construction, values a property based on how much it would cost to replace it with an identical or similar property, taking into account the cost of materials and labor required to reproduce or replace the property, minus depreciation. Lastly, the income approach, typically used for investment or commercial properties, values a property based on its income. It calculates the net present value of future income streams given the yield an investor would expect for that investment. Depending on the property's nature and data availability, these methods can sometimes be combined. However, the specifics can vary greatly depending on local laws and practices, so it's best to contact the local assessor's office for the most accurate information. Each method would require different data collection: Each method of property assessment requires specific types of information or measurements to complete the evaluation: Market Approach: For the market approach, the following measurements are typically required: The recent sale prices of comparable properties in the same area. Characteristics of the property, including the number of bedrooms and bathrooms, square footage, lot size, age, and the property's condition. Cost Approach: For the cost approach, these measurements are needed: The cost to construct a similar property, including labor and materials. This might require data on local construction costs and labor rates. The value of the land as if it were vacant. This could be based on the sale prices of comparable land parcels in the same area. The depreciation of the property could be due to physical wear and tear, functional obsolescence (e.g., an outdated layout), or external factors (e.g., changes in the neighborhood). Income Approach: For the income approach, you would need: The potential income the property could generate. For rental properties, this would be the potential rental income. For businesses, it could be the income the business generates. The operating expenses of the property, such as maintenance, utilities, insurance, and property management costs. The capitalization rate is the rate of return a reasonable investor would expect on the property type. This could be based on rates for comparable properties.

Data Type #1—Spatial Data—these data have a geographic component and are important for property valuation. Usually, they can be generated through GIS tools if not already available. Parcel Boundaries: These are the specific outlines of each property. They typically come in the form of shapefiles and indicate the precise geographical boundaries of a property. Future Development Plans: Information about any future infrastructure or development plans in the area. These could significantly impact the future value of a property. Satellite Images: High-resolution satellite images can provide a lot of useful information about a property, including the size and shape of buildings, the presence of features like pools or solar panels, and the general condition of the property. Topographic Data: This could include Digital Elevation Models (DEMs) that show the elevation of the land, which could affect property value. Land Use Data: This shows how land in the area is used (residential, commercial, industrial, agricultural, etc.). Land use could have a significant impact on property values. Road Networks: Proximity to transportation infrastructure can influence a property's value. This includes public transit data stations or stops, and proximity to public transportation can be a significant factor in property valuation. Environmental Data: This might include flood zones, proximity to bodies of water, forest cover, soil quality, etc. Amenities: Locations of schools, parks, shopping centers, hospitals, and other amenities.

Data Type #2—Non-Spatial Data—these are data that don't have a geographic component but are still important for property valuation: Structural Characteristics: This might include the age of the buildings, their square footage, the number of rooms, the types of materials used, and other physical characteristics. Building Codes Compliance: Details about the building's compliance with local codes can impact its value. This could include information about any code violations and the results of any inspections. Rental History: If a property has been rented out in the past, information about the rental history can be useful. This could include the length of each rental period, the amount of rent charged, and the reliability of the tenants. Ownership Details: This might include the owner's name, purchase date, purchase price, etc. Sale History: Information about previous property sales, including dates and prices. Tax Records: Past tax assessments and payments could be useful for predicting future tax amounts. Local Market Data: Data about recent sales of comparable properties in the area. Zoning Regulations: The property's zoning designation and what kind of structures or businesses are allowed there. Environmental Efficiency Ratings: These can include ratings related to energy efficiency, such as LEED ratings or other environmental impacts of the property. Disaster History: Information about any natural disasters that have affected the property. This could include floods, earthquakes, wildfires, etc. Crime Statistics: Detailed data about the area's prevalence and types of crime can significantly impact a property's value. Liens and Judgments: Any liens or judgments against the property could affect its marketability and value.

When collecting data, ensuring ISO or INSPIRE compliance for data can be important for interoperability, standardization, and data sharing. While it's crucial to consult the specific requirements of ISO or INSPIRE for your region or project, here are some general considerations for data formats and importing into open-source GIS software like QGIS.

Spatial Data Infrastructure (SDI) Considerations—ISO (International Organization for Standardization) standards play a critical role in guiding the creation and management of Spatial Data Infrastructure (SDI). Here's how the data could be formulated with references to relevant ISO standards:

Data Availability (ISO 19115: Metadata): According to this standard, there should be adequate metadata to describe the available spatial data for the required geographic areas. This ensures that users can find the appropriate datasets and understand their content, source, and usability. Data Accuracy (ISO 19157: Data Quality): This standard provides a framework for specifying and reporting data quality, including accuracy. The SDI will ensure the spatial data is accurate and up-to-date, with quality measures implemented according to this standard. Interoperability (ISO 19119: Services): This standard defines how GIS and ‘services’ interact. This assumes that there is an ambition to integrate with other systems used by tax authorities, for example—in such cases, the data can integrate effectively with other systems used by tax authorities, promoting seamless data sharing and usage. Sustainability (ISO 19101: Reference Model): This standard provides a framework for establishing and maintaining an SDI. It will guide the project in ensuring the system is robust, maintained, and updated over time to remain relevant and useful.

Schematic Mapping of Existing Data: This process entails visualizing or representing existing spatial data in an organized, intuitive, and easily understandable format. This can include various geographic and spatial data an organization may have collected over time. It provides an overview of the current data situation and can help to identify patterns, relationships, or discrepancies that might not be readily apparent from raw data. Schematic Mapping of Periodic Reports: Periodic reports contain valuable insights into temporal changes, trends, and patterns. Schematic mapping of such reports visually represents these insights over time, often revealing otherwise hidden temporal patterns or changes. This makes it easier for decision-makers to understand trends and make informed decisions. Schematic Mapping of Scheme of Service of Selected Institutions: This pertains to the visual representation of how different services of an organization or institutions are interconnected spatially, how they function, and how they can influence one another. In an SDI context, it can map the flow of spatial data within and between organizations, highlighting any bottlenecks, inefficiencies, or potential areas for improvement.

Designing Metadata and Schema Structure—setting up the structure of your geodatabase, defining how different datasets will interact, and establishing standards for metadata. This step ensures data organization, consistency, and accessibility throughout the GIS application

The first step involves designing the metadata and schema structure, which entails setting up the structure of the geodatabase—a database designed to store, query, and manipulate geographic information and spatial data. It's a type of database that integrates geographic data (spatial data), attribute data, and spatial relationships between different datasets, providing a more robust framework for managing spatial data compared to traditional flat file storage methods, defining how the different datasets will interact, and establishing standards for metadata.

Following this, you must establish metadata standards, with INSPIRE and ISO 19115 commonly used for geospatial data

After deciding on the standards, creating metadata templates based on the chosen standards becomes crucial. Documenting your data collection and entry procedures also forms a significant part of the workflow. The last part of this stage involves implementing data quality controls by establishing procedures to check your data's quality

The output of this process is a structured geodatabase, comprehensive metadata standards, and data collection and quality control procedures.

Data Collection and Digitization: In this step, you will collect spatial and non-spatial data from various sources. The accuracy and comprehensiveness of your data collection efforts are essential for accurate property assessment and tax calculations. Highlight the need for thorough planning and testing within the workflow to address challenges that may arise when integrating different open-source software components.

Identify Data Sources: Identify the sources from which you will collect data. This can include local government databases, open-source data platforms, public records, surveys, or field data collection.

Collect Spatial Data: Collect the necessary spatial data for property assessment. This may involve acquiring parcel boundaries, future development plans, satellite images, topographic data, land use data, road networks, environmental data, and other relevant spatial information. QGIS is commonly used for handling and processing spatial data. It provides various data collection, analysis, and validation tools.

Collect Non-Spatial Data: Collect the non-spatial data pertinent to property assessment. This can include information such as structural characteristics of properties, rental history, ownership details, sale history, tax records, local market data, zoning regulations, environmental efficiency ratings, disaster history, crime statistics, liens and judgments, and other relevant data. This is a foundational step. If you have physical paper records, you must manually input this data or use scanning and Optical Character Recognition (OCR) technology to digitize the records. Check the digitized data for any errors or discrepancies. Data in Excel or other digital formats can be compiled for easier processing in the next steps. For OCR, you can use tools like Adobe Acrobat or Tesseract. You can use Excel or a similar spreadsheet tool to organize digital data. Tools like spreadsheets or databases (e.g., Microsoft Excel or PostgreSQL) can also be used for managing and cleaning non-spatial data.

Define the Data Structure: Abiding by ISO 19107 - Geographic information - Spatial schema, the first step is to define the structure of your geodatabase. To do this, you must fully understand your existing data, which can be achieved through schematic mapping. This mapping should visually represent how your current data is structured and highlight any potential areas of inefficiency or redundancy. This step also involves generating periodic reports, which help monitor the state of your data and provide insight into how it's changing over time, facilitating iterative improvements in your data structure. Also, this stage includes a schematic mapping of the scheme of service of selected institutions. By understanding these services, you can identify gaps in your data or potential new data sources. By identifying these, you're not only defining but also refining the structure of your geodatabase. The open-source tool for schematic mapping in this step is QGIS. The open-source relational database PostgreSQL and the geospatial extension PostGIS are the chosen tools for defining the database structure.

Establish Metadata Standards: Leveraging ISO 19115 - Geographic Information - Metadata, decide on the metadata standards to use. These standards will guide the formatting and classification of your geospatial data. The choice of standards should also consider the schematic mapping of the scheme of service of relevant institutions. Understanding these various service schemes gives you a clear view of what metadata standards would best suit your needs and ensure effective communication and data interoperability with these institutions. GeoNetwork, a comprehensive catalog system for managing spatially referenced resources, is an open-source tool for managing metadata by established standards.

Create Metadata Templates: Utilizing ISO 19110 - Geographic information - Methodology for feature cataloging, create metadata templates that adhere to your chosen standards. Part of this step involves developing a conceptual schema for digital information dashboards. This conceptual schema acts as a blueprint, showing how data will be organized and presented on your dashboards. This organization must align with the metadata templates, ensuring that the data is well-structured and can be effectively visualized for end users. Metatools, an extension of QGIS, is the open-source tool recommended for creating and managing metadata templates in this step. For designing digital dashboards based on metadata templates, Apache Superset, an open-source data exploration and visualization platform, is to be used.

Document Data Collection Procedures: Following ISO 19157 - Geographic information - Data quality, document your data collection and entry procedures. By understanding the scheme of service of relevant institutions, you can tailor your data collection procedures to harmonize with these services. These documented procedures should be collated into a comprehensive handbook describing your approach to digitizing non-digital data. This handbook serves as a go-to resource, ensuring consistency in data handling practices and offering clear guidance to all involved in the data collection process. LibreOffice Writer, an open-source word processor, will create comprehensive handbooks and document data collection procedures. The open-source mobile app, Input, based on QGIS, is recommended for systematic data collection as per established procedures.

Implement Data Quality Controls: Implement robust data quality control measures based on ISO 19138 - Geographic Information - Data Quality Measures. This involves defining a quality assurance approach to digitalizing existing non-digital data, ensuring the digitized data retains its accuracy and reliability. Moreover, these measures should extend to checking data resulting from periodic reports, contributing to an ongoing effort to maintain the integrity of the data in your geodatabase. Furthermore, organize hands-on training sessions for your team to understand and execute these quality control procedures effectively. This not only increases the proficiency of your team but also helps in maintaining consistent, high-quality data across your geodatabase. QGIS, with its built-in functionalities for data cleaning and quality control, is the tool of choice in this step. Moodle, a widely used open-source learning management system, is the chosen tool for creating and managing hands-on training modules to ensure the effective implementation of quality control procedures.

Do: Take the time to plan and design the geodatabase structure carefully. Consider scalability and flexibility to accommodate future data updates and changes. Invest time and resources in designing a well-structured geodatabase and establishing clear, comprehensive metadata standards. This will save you a lot of trouble down the line. Don't: Neglect the importance of metadata. Well-documented metadata improves data discoverability, ensures data integrity, and facilitates data sharing. Don't neglect data quality controls. Regular checks for data quality are crucial for maintaining the accuracy and reliability of your geodatabase. Output: A well-defined data structure, established metadata standards, metadata templates, documented data collection procedures, and implemented data quality controls. These outputs will be the foundation for effectively organizing and managing the GIS data. Don’t forget: Step 1.1: Define Data Structure—define the structure of your geodatabase. Determine what tables you need, their attributes, and how they relate. Ensure your structure will support all the data you need for your property assessments. Step 1.2: Establish Metadata Standards—decide on the metadata standards you will use. INSPIRE and ISO 19115 are commonly used standards for geospatial data. Your metadata should include information about when and how the data was collected, who collected it, what geographic area it covers, and any restrictions on its use. Step 1.3: Create Metadata Templates—create templates for your metadata based on your chosen standards. This will ensure consistency across all your datasets. Step 1.4: Document Data Collection Procedures—document your data collection and entry procedures. This will help ensure your data is collected and entered consistently and accurately. Step 1.5: Implement Data Quality Controls—establish procedures for checking the quality of your data. This could involve regular checks for errors in data entry, consistency across different datasets, and checks to ensure that your data matches reality.

Data Cleaning and Validation: Conduct data cleaning and validation procedures after collecting data. Remove duplicate entries, correct errors, and fill in missing values. Validate the accuracy and integrity of the data by cross-referencing different data sources and conducting quality control checks. Do: Ensure data accuracy and completeness through thorough data collection efforts. Validate the collected data against reliable sources and employ quality control measures. Don't: Overlook the importance of data cleaning and validation. Inaccurate or incomplete data can lead to erroneous assessments and calculations. Don't: Rely solely on a single data source. Cross-reference and validate data from multiple sources to ensure accuracy and completeness. Using diverse and reliable data sources enhances the reliability of your property assessments and tax calculations. Output: A comprehensive collection of spatial and non-spatial data relevant to property assessment and tax calculations. The collected data will serve as the basis for subsequent analysis and processing steps in the GIS application.

Geodatabase Creation and Data Import—Integration of Spatial and Non-Spatial Datasets within the Geodatabase: In this step, you will import both the spatial and non-spatial data into the Geodatabase and establish relationships between different datasets. Data integration allows for efficient management and analysis of the collected data. PostgreSQL and PostGIS are commonly used for data integration. PostgreSQL provides a robust relational database management system, while PostGIS adds spatial capabilities to handle spatial data effectively. Highlight the importance of a well-structured workflow for maintaining quality control, reproducibility, and managing updates to open-source software without disruption.

Import Spatial Data: Use PostgreSQL and PostGIS to import the spatial data into the geodatabase. This includes importing digitized parcel boundaries, satellite images, topographic data, road networks, environmental data, and other relevant spatial datasets. Create appropriate tables and define the necessary attributes to store the spatial data.

Import Non-Spatial Data: Import non-spatial data, such as property characteristics, ownership details, rental history, tax records, and other relevant information. Use PostgreSQL to create tables and define the appropriate fields to store the non-spatial data. Establish Relationships: Identify the relationships between different datasets within the geodatabase. Establish primary and foreign key relationships to link the spatial and non-spatial data tables. This will enable efficient querying and analysis of the integrated data.

Property Assessment: In this step, you will conduct property assessments based on the available data and methodologies. Property assessment involves determining the market value of properties based on various factors such as size, condition, location, and comparable sales. You can use QGIS, PostgreSQL, and spreadsheets for data analysis and calculations to conduct property assessments. These tools provide functionality for organizing and analyzing property data, performing calculations, and recording assessment results.

Firstly, it's crucial to thoroughly understand the objectives of the Web/Mobile GIS application, the target audience, the type of data you'll be working with, and the specific features the end-users will need. Do they need to be able to interact with the data? If so, how? Should they be able to filter or search the data? Answering these questions upfront will help guide your design and development process. Don't rush this process or make assumptions about what the end users need. Communicate effectively with all stakeholders and consider getting their feedback at multiple stages throughout the project. There are really three building blocks to realizing a well-designed application: Block A—Setting Up the Geospatial Server: A geospatial server such as GeoServer or MapServer is needed to serve your geospatial data over the web, and finally, Block B—Designing the Web GIS Application: This phase involves planning and sketching the layout, functionalities, and overall user experience of the web or mobile application and creating a client-side Interactive Map Interface: This would require software like Leaflet, which is a powerful open-source JavaScript library for mobile-friendly interactive maps.

Installation of GeoServer: A GeoServer is a Java-based software server that allows users to view and edit geospatial data. Using open standards set by the Open Geospatial Consortium (OGC), GeoServer allows great flexibility in map creation and data sharing. GeoServer's capabilities extend to on-the-fly rendering of geospatial data to images for visualization on a map, making it easier to depict intricate geographic data within your application. Moreover, it's designed for performance and scalability, which makes it well-suited for high-demand scenarios, offering options for caching and clustering. While it's possible to create a web or mobile app without a GeoServer if your app requires interaction with intricate geospatial data, geographic queries, or rendering geospatial data on a map, it can simplify and optimize these tasks. There are several open-source geospatial servers available. We discussed them above. GeoServer is perhaps the most popular open-source server designed to serve geospatial data. MapServer is another popular open-source platform for publishing spatial data and interactive mapping applications to the web. It's known for its speed and reliability. We can go with PostGIS, which, while technically an extension to the PostgreSQL database, PostGIS adds geospatial capabilities to the database, effectively allowing it to serve as a geospatial data server. It's particularly known for its powerful spatial database capabilities. To start with the installation, first, ensure that you have Java installed on your system. GeoServer requires a Java Runtime Environment (JRE), and it's recommended to use a version that GeoServer officially supports. Download and install GeoServer from the official website. The installation process is relatively straightforward, with installers available for various operating systems. After installation, you can start GeoServer by running the startup script that came with the installation. This will launch GeoServer, which by default runs on port 8080. By navigating to http://localhost:8080/geoserver, you can access the GeoServer web admin interface to start managing your geospatial data.