Parcel Data Scripts for Standardization and Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Companies face difficulties in consolidating parcel data from various sources into a standardized format, leading to inefficiencies in data retrieval and utilization for entities like banks, insurance companies, and real estate agents due to the disparate nature of information sources.
Innovation Solution
The use of scripts to standardize and normalize parcel data by converting, cleaning, and formatting it into a common format within a database, utilizing ESRI's modeling environment, and employing tracking applications to manage data sources and assignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If parcel data is collected from multiple external sources, then data completeness improves, but data standardization difficulty increases
Solution Approach 1:
The patent introduces an intermediary data standardization layer that mediates between multiple external data sources and the internal system. This intermediary layer includes standardized data fields, validation rules, and transformation logic that convert diverse external formats into a unified internal representation, resolving the contradiction by providing a buffer that handles variability without propagating it throughout the system
Solution Approach 2:
The system dynamically adjusts data parameters during ingestion by automatically detecting source-specific formats and transforming them to standardized parameters. This includes mapping different field names, data types, and structures to a common schema, allowing the system to maintain data completeness from multiple sources while managing standardization through automated parameter transformation
2Reliability
If manual data cleaning and validation is performed, then data quality improves, but processing time increases
Solution Approach 1:
The system performs preliminary data cleaning and validation actions at the point of data ingestion, before data is fully processed or stored. This includes pre-validation of required fields, format checking, and basic anomaly detection during the data loading phase, which prevents poor quality data from entering the system and reduces the need for later manual intervention
Solution Approach 2:
The patent implements self-service data validation mechanisms where the system automatically detects and corrects common data quality issues without manual intervention. This includes automated duplicate detection, format standardization, and validation rule enforcement that enable the system to maintain high data quality while minimizing the time investment required for manual cleaning
3Loss of information
If comprehensive tracking of data sources is implemented, then data provenance improves, but system complexity increases
Solution Approach 1:
The tracking system is segmented into modular components that handle different aspects of data provenance independently. Each data source has its own tracking metadata structure, and the system maintains separate logs for data ingestion, transformation, and validation events. This segmentation allows comprehensive tracking without creating a monolithic complex system
Data Source
AI summary
In some embodiments, scripts may be used to perform parcel data acquisition, conversion, and clean-up/repair in an automated manner and/or through graphical user interfaces. The scripts may be used, for example, to repair geometries of new parcel data, convert multi-part parcel geometries to single part parcel geometries (explode), eliminate duplicate parcel geometries, append columns, create feature classes, and append feature classes. These scripts may be executed in a predetermined manner to increase efficiency. In some embodiments, different combinations of attributes may be appended to stored parcel data. In some embodiments, a tracking application may be used to track information about sources of data. In some embodiments, a tracking application may be used to track which system users are assigned to specific tasks (e.g., in a data acquisition project).