Local Geography of Row-Crop Quality Land and Cropland Cash Rental Rates

While farmland rental markets are likely to be spatially differentiated, the fine spatial structure of row-crop quality land should have a significant effect on cash rent determination. This study provides a rigorous empirical understanding of the effect of land spatial heterogeneity on cash rental rates. The lacunarity index is employed to measure spatial heterogeneity of land quality, which is built directly upon a soil quality measure, the land parcel’s corn suitability rating index (CSR). A panel data random effect model is applied on annual survey data of farmland cash rental rates of Iowa for 1987-2009. As expected, land spatial heterogeneity has a statistically significant and negative effect on local cash rent rates. The effect’s origin warrants further research.land spatial heterogeneity, rental market, Agricultural Finance, C5, G1, Q1,

Ranking spatial data by quality preferences

A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to different parameters. © 2006 IEEE.published_or_final_versio

On Quantifying Qualitative Geospatial Data: A Probabilistic Approach

Living in the era of data deluge, we have witnessed a web content explosion, largely due to the massive availability of User-Generated Content (UGC). In this work, we specifically consider the problem of geospatial information extraction and representation, where one can exploit diverse sources of information (such as image and audio data, text data, etc), going beyond traditional volunteered geographic information. Our ambition is to include available narrative information in an effort to better explain geospatial relationships: with spatial reasoning being a basic form of human cognition, narratives expressing such experiences typically contain qualitative spatial data, i.e., spatial objects and spatial relationships. To this end, we formulate a quantitative approach for the representation of qualitative spatial relations extracted from UGC in the form of texts. The proposed method quantifies such relations based on multiple text observations. Such observations provide distance and orientation features which are utilized by a greedy Expectation Maximization-based (EM) algorithm to infer a probability distribution over predefined spatial relationships; the latter represent the quantified relationships under user-defined probabilistic assumptions. We evaluate the applicability and quality of the proposed approach using real UGC data originating from an actual travel blog text corpus. To verify the quality of the result, we generate grid-based maps visualizing the spatial extent of the various relations

Quality of life in the regions: An exploratory spatial data analysis for West German labor markets

Which of Germanys regions is the most attractive? Where is it best to live and work - on objective grounds? These questions are summed up in the concept quality of life. This paper uses recent research projects that determine this parameter to examine the spatial distribution of quality of life in Germany. For this purpose, an Exploratory Spatial Data Analysis is conducted which focuses on identifying statistically significant (dis-)similarities in space. An initial result of this research is that it is important to choose the aggregation level of administrative units carefully when considering a spatial analysis. The level plays a crucial role in the strength and impact of spatial effects. In concentrating on various labor market areas, this paper identifies a significant spatial autocorrelation in the quality of life, which seems to be characterized by a North-Mid-South divide. In addition, the ESDA results are used to augment the regression specifications, which helps to avoid the occurrence of spatial dependencies in the residuals. --Quality of Life,Exploratory Spatial Data Analysis,Functional Economic Areas,Spatial Econometrics,LISA Dummies

W42 - a scalable spatial database system for holding Digital Elevation Models

The design of a scalable system for holding spatial data in general and digital elevation models (DEMs) in specific has to account for the characteristics of data from various application fields. The data can be heterogeneous in coverage, as well as in resolution, information content and quality. A database aiming at the representation of world-wide DEMs has to consider these differences in the design of the system with respect to the structure and the algorithms. The database system W42, which is presented in the work at hand, is a scalable spatial database system capable of holding, extracting, mosaicking, and fusing spatial data represented in raster- as well as in vector-format. Design aspects for this task can be specified as holding spatial data in unique data structures and providing unique access functions to the data. These are subject of this work as well as first experiences gained from the implementation of part of the extensions made for the TanDEM-X mission

A quality-aware spatial data warehouse for querying hydroecological data

International audienceAddressing data quality issues in information systems remains a challenging task. Many approaches only tackle this issue at the extract, transform and load steps. Here we define a comprehensive method to gain greater insight into data quality characteristics within data warehouse. Our novel architecture was implemented for an hydroecological case study where massive French watercourse sampling data are collected. The method models and makes effective use of spatial, thematic and temporal accuracy, consistency and completeness for multidimensional data in order to offer analysts a âdata qualityâ oriented framework. The results obtained in experiments carried out on the Saône River dataset demonstrated the relevance of our approac