Mixed-methods designs, especially those in which case selection is regression-based, have become popular across the social sciences. In this paper, we highlight why tools from spatial analysis—which have largely been overlooked in the mixed-methods literature—can be used for case selection and be particularly fruitful for theory development. We discuss two tools for integrating quantitative and qualitative analysis: (1) spatial autocorrelation in the outcome of interest; and (2) spatial autocorrelation in the residuals of a regression model. The case selection strategies presented here enable scholars to systematically use geography to learn more about their data and select cases that help identify scope conditions, evaluate the appropriate unit or level of analysis, examine causal mechanisms, and uncover previously omitted variables.

Sangria includes two main datasets: each contains Gaussian instrumental noise and simulated waveforms from 30 million Galactic white dwarf binaries, from 17 verification Galactic binaries, and from merging massive black-hole binaries with parameters derived from an astrophysical model. The first dataset includes the full specification used to generate it: source parameters, a description of instrumental noise with the corresponding power spectral density, LISA's orbit, etc. We also release noiseless data for each type of source, for waveform validation purposes. The second dataset is blinded: the level of istrumental noise and number of sources of each type are not disclosed (except for the known parameters of the verification binaries).

See LDC website for more details.

Spatial weights matrices used in quantitative geography furnish maps with their individual latent eigenvectors, whose geographic distributions portray distinct spatial autocorrelation (SA) components. These polygon patterns on maps have specific meaning, partially in terms of geographic scale, which this article describes. The goal of this description is to enable spatial analysts to better understand and interpret these maps individually, as well as mixtures of them, when accounting for SA in a spatial analysis. Linear combinations of Moran eigenvector maps supply a powerful and relatively simple tool that can explain SA in regression residuals, with an ability to render reasonably accurate reproductions of empirical geographic distributions with or without the aid of substantive covariates. The focus of this article is positive SA, the most commonly encountered nature of autocorrelation in georeferenced data. The principal innovative contribution of this article is establishing a better clarification of what the synthetic SA variates extracted from spatial weights matrices epitomize with regard to global, regional, and local clusters of similar values on a map. This article shows that the Getis-Ord Gi* statistic provides a useful tool for classifying Moran eigenvector maps into these three qualitative categories, illustrating findings with a range of specimen geographic landscapes.

This dataset provides information about the number of properties, residents, and average property values for Lisa Lane cross streets in West Orange, TX.

Financial overview and grant giving statistics of Mark A And Lisa J Walsh Foundation

This dataset provides information about the number of properties, residents, and average property values for Lisa Place cross streets in Orange, CA.

This dataset provides information about the number of properties, residents, and average property values for Lisa Lane cross streets in Athens, NY.

This dataset provides information about the number of properties, residents, and average property values for Lisa Circle cross streets in Madison, MS.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The LISA company data contain information on Dutch companies, aggregated on a municipal or other level, or in the form of microdata. The data have a geographical (location) and socioeconomic component (employment in a sector).

Financial overview and grant giving statistics of Lisa And Maury Friedman Foundation

This data package was created 2025-01-14 15:12:40 by NPSTORET and includes selected project, _location, and result data. Data were collected in advance of a Ph.D. proposal by Lisa Chang. This work ultimately led to Chang's University of Virginia dissertation entitled 'Carbon and nitrogen effects on nitrification in Shaver Hollow Watershed, Shenandoah National Park'. Data contained in Shenandoah National Park - University of Virginia NPSTORET back-end file (NPS_UVA_NPSTORET_BE_20250108.ACCDB) were filtered to include: Project: - SHEN_UVA_CHANG_1994: Lisa Chang’s Pre-Ph.D. Dissertation Research Data from Shaver Hollow Station: - Include Trip QC And All Station Visit Results Park/Unit Code: - SHEN Value Status: - Accepted or Certified (exported as Final) or Final The data package is organized into five data tables: - Projects.csv - describes the purpose and background of the monitoring efforts - Locations.csv - documents the attributes of the monitoring locations/stations - Results.csv - contains the field measurements, observations, and/or lab analyses for each sample/event/data grouping - HUC.csv - enumerates the _domain of allowed values for 8-digit and 12-digit hydrologic unit codes utilized by the Locations datatable - Characteristics.csv - enumerates the _domain of characteristics available in NPSTORET to identify what was sampled, measured or observed in Results Period of record for filtered data is 1994-06-15 to 1994-12-03. This data package is a snapshot in time of one National Park Service project. The most current data for this project, which may be more or less extensive than that in this data package, can be found on the Water Quality Portal at: https://www.waterqualitydata.us/data/Result/search?project=SHEN_UVA_CHANG_1994

Financial overview and grant giving statistics of Live Like Lisa Foundation Inc.

This dataset provides information about the number of properties, residents, and average property values for Lisa Lane cross streets in Dousman, WI.

The TDI data and PSD/sensitivity-related files for PyCBC LISA documentation example, most of them are generated from LDC-Sangria dataset.

Building a Foundation for Linear Infrastructure Safeguards in Asia (LISA) was a 14-month project that sought to understand Asia’s existing capacity to implement wildlife-friendly linear infrastructure, as well as the challenges and barriers that slow its adoption, to protect Asia’s diverse wildlife species and their critical habitats from the region’s rapid infrastructure development. This data asset contains the data collected from a survey of 320 respondents regarding Asia’s capacity for implementing wildlife-friendly linear infrastructure as part of this project in May and June of 2021. The survey was targeted at individuals in five focal countries (Bangladesh, India, Mongolia, Nepal, and Thailand) and four main constituent groups (government, industry [engineering, construction, & consulting firms], international financial institutions [IFIs], and nongovernmental organizations). Some IFI respondents were based out of regional headquarters and were thus outside of the five focal countries. Respondents were identified through opportunistic sampling, with the survey being sent directly to 840 individuals across all sectors and countries, with the instruction to pass along to other colleagues as appropriate.

This dataset provides information about the number of properties, residents, and average property values for Lisa Lane cross streets in North Reading, MA.

Posterior samples and code to reproduce all figures associated with Discovering neutron stars with LISA via measurements of orbital eccentricity in Galactic binaries.

The parameter_estimation folder contains the following:

• campaigns: Analyses of eccentric quasi-monochromatic binaries, gridding over gravitational-wave frequency, eccentricty, and SNR. See the README inside for more information. The resulting posteriors are used in Figure 3, and the fitting formula Eq. 21.
• fiducial_source_checks: Analyses that vary parameters other than SNR and frequency to investigate the effect on the minimum eccentricity that can be recovered. Used in Figure A1. Posteriors used for Figure 4 are also found in the golden_binary folder.
• nhat_runs: Various analyses used for Figures 5, 6, B1, and C1. See the README inside for more information. Also see the README in eccentric_gb_scripts and links therein.

Within each parameter estimation output folder there are .dat files for quantities such as the source SNR, log evidence, and posterior. There are also configuration .yaml files which are used by the BALROG code. These contain:

• lisa_config: Parameters describing the LISA mission, including the duration in seconds.
• nessai_opts: Settings used by nessai (the sampler used in this work).
• priors: Lower and upper limits used for each source parameter.
• sources: Injected values for each source parameter.

The notebooks folder contains code to produce Figures 2, 3, 4, 6, and A1. Also included are notebooks to produce the fitting formula Eq. 21 (emin_grid.ipynb), and to inspect analyses in the campaigns and fiducial_source_checks folders.

The eccentric_gb_scripts folder contains code to produce Figures 1, 5, B1 and C1. See the README inside for more information.

Historical Dataset of Lisa Academy West Elementary School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2017-2023),Total Classroom Teachers Trends Over Years (2017-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2017-2023),Asian Student Percentage Comparison Over Years (2017-2023),Hispanic Student Percentage Comparison Over Years (2017-2023),Black Student Percentage Comparison Over Years (2017-2023),White Student Percentage Comparison Over Years (2017-2023),Two or More Races Student Percentage Comparison Over Years (2017-2023),Diversity Score Comparison Over Years (2017-2023),Free Lunch Eligibility Comparison Over Years (2017-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2017-2023),Reading and Language Arts Proficiency Comparison Over Years (2017-2022),Math Proficiency Comparison Over Years (2017-2022),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2017-2022)

Comparison of between normalized LISA and the equivalent transformation results of Anselin’s second set of LISA definitions.