HyperSmooth: A System for Interactive Spatial Analysis Via Potential Maps

Plumejeaud, Christine; Vincent, Jean-Marc; Grasland, Claude; Bimonte, Sandro; Mathian, Hélène; Guelton, Serge; Boulier, Joël; Gensel, Jérôme

doi:10.1007/978-3-540-89903-7_2

Cited by 6 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Implementation in SOLAP tiers (Spatial datawarehouse tier, SOLAP Server tier, SOLAP Client tier): Field data is supported in each layer of the SOLAP architecture by providing ad-hoc functionalities. Indeed, existing SOLAP servers do not support modelling and querying field data [38], SOLAP clients provide only interactive vector maps [37], and Spatial DBMSs do not support all kinds of field data (complete and incomplete).…”

Section: Discussionmentioning

confidence: 99%

OLAPing Field Data: a Theoretical and Implementation Framework

Bimonte¹,

Kang

Paolino

et al. 2014

Fundamenta Informaticae

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

OLAPing Field Data: a Theoretical and Implementation Framework

Bimonte¹,

Kang

Paolino

et al. 2014

Fundamenta Informaticae

Self Cite

View full text Add to dashboard Cite

show abstract

“…We plan to work on the implementation of the model in a ROLAP architecture. This implies the definition of: (i) query languages for OLAP server [18] for field data [12], (ii) indexes [20] and pre-aggregation techniques [19] for spatial data warehouses using field dimensions and measures, and (iii) interactive field maps [17] for SOLAP clients. Figure 7: Instance of a Field View Structure…”

Section: Discussionmentioning

confidence: 99%

Towards a Model for the Multidimensional Analysis of Field Data

Bimonte

Kang

2010

Advances in Databases and Information Systems

Self Cite

View full text Add to dashboard Cite

Integration of spatial data into multidimensional models leads to the concept of Spatial OLAP (SOLAP). Usually, SOLAP models exploit discrete spatial data. Few works integrate continuous field data into dimensions and measures. In this paper, we provide a multidimensional model that supports measures and dimension as continuous field data, independently of their implementation.It has been estimated that about 80% of the data stored in corporate databases integrates geographic information [7]. This information is typically represented according two models, depending on the nature of data: discrete (vector) and field [22]. The latter model represents the space as a continuous field. Fields have to be discretized to be represented into computers according to data input, and data analysis. These representations can be grouped into two categories: incomplete and complete. Incomplete representations store only some points and need supplementary functions to calculate the field in non-sampled areas. Complete representations associate estimated values to regions and assume that this value is valid for each point in the regions (raster). Fields are very adapted for modeling spatial phenomena such as pollution, temperature, etc. They allow a point by point analysis through the Map Algebra operators [16] [22].In order to benefit from Data warehousing and OLAP decision support technologies [11] also in the context of spatial data, some works extended them leading to the concept of Spatial OLAP (SOLAP), which integrates OLAP and Geographic Information Systems (GIS) functionalities into a unique framework [2]. As for the model underlying SOLAP systems, several research issues from theoretical and implementation point of view have risen. Indeed, several works focus on indexing [20] and visualization techniques [6]. Motivated by the relevance of a formal

show abstract

“…We infer that property markets are discrete social data, similar to Tobler’s hypothesis [130]: a potential price for a specific location is a function of distance to nearby similar transactions, and also a function of the number of properties available, turnover and realized transactions. This method removes spatial bias, resolving the Modifiable Areal Unit Problem (MAUP), as demonstrated for demographic indices in Europe [127, 131]. We elaborate on Grasland’s framework for spatial analysis of social facts [129], based on Tobler’s first law of geography [132] and Stouffer’s intervening opportunities [133], justifying to use Stewart’s potential [128] for the spatial interpolation of social discrete data (details in S1 Methodological Appendix).…”

Section: Methodology: An Empirical Analysis Of Transactions In Paris mentioning

confidence: 99%

Who sells to whom in the suburbs? Home price inflation and the dynamics of sellers and buyers in the metropolitan region of Paris, 1996–2012

et al. 2019

View full text Add to dashboard Cite

Price inflation has outbalanced the income of residents and buyers in major post-industrial city-regions, and real estate has become an important driver of these inequalities. In a context of a resilient inflation of home values during the last two decades in the greater Paris Region, it is critical to examine housing price dynamics to get a better understanding of socioeconomic segregation. This paper aims at presenting spatial analysis of the dynamics of segregation pertaining to inflation, analyzing price and sellers and buyers data. Using interpolation techniques and multivariate analysis, the paper presents a spatial analysis of property-level data from the Paris Chamber of Notaries (1996-2012) in a GIS (159,000 transactions in suburban areas, single family homes only). Multivariate analysis capture price change and local trajectories of occupational status, i.e. changes in balance between inward and outward flows of sellers and buyers. We adopt a method that fits the fragmented spatial patterns of suburbanization. To do so, we remove the spatial bias by means of a regular 1-km spatial grid, interpolating the variables within it, using a time-distance matrix. The main results are threefold. We document the spatial patterns of professionalization (a rise of executives, intermediate occupation and employees) to describe the main trends of inward mobility in property ownership in suburbs, offsetting the outward mobility of retired persons. Second, neighborhood trajectories are related the diverging patterns of appreciation, between local contexts of accumulation with a growth of residential prices, and suburbs with declining trends. The maturity of suburbanization yields a diversified structure of segregation between the social groups, that do not simply oppose executives vs. blue collar suburbs. A follow-up research agenda is finally outlined.

show abstract

HyperSmooth: A System for Interactive Spatial Analysis Via Potential Maps

Cited by 6 publications

References 4 publications

OLAPing Field Data: a Theoretical and Implementation Framework

OLAPing Field Data: a Theoretical and Implementation Framework

Towards a Model for the Multidimensional Analysis of Field Data

Who sells to whom in the suburbs? Home price inflation and the dynamics of sellers and buyers in the metropolitan region of Paris, 1996–2012

Contact Info

Product

Resources

About