This collection of computer programs and test data files was compiled by the Census Bureau for use with GEOGRAPHIC BASE FILE/DUAL INDEPENDENT MAP ENCODING (GBF/DIME), 1980 (ICPSR 8378). This collection consists of files grouped into five categories: Special Program Information Tape (SPIT) Datasets, UNIMATCH System Datasets, ADMATCH System Datasets, EASYMAP System Datasets, and EASYCORD System Datasets. Some of the capabilities of the programs in this collection include: mapping files for which complicated data manipulation is required, generating individualized lists of candidates for carpools, linking of records on the basis of street address, creating shaded area maps for statistical display, and producing a map coordinate system.

September 1., 2016 REPLICATION FILES FOR «THE IMPACT OF STATE TELEVISION ON VOTER TURNOUT», TO BE PUBLISHED BY THE BRITISH JOURNAL OF POLITICAL SCIENCE The replication files consist of two datasets and corresponding STATA do-files. Please note the following: 1. The data used in the current microanalysis are based on the National Election Surveys of 1965, 1969, and 1973. The Institute of Social Research (ISF) was responsible for the original studies, and data was made available by the NSD (Norwegian Center for Research Data). Neither ISF nor NSD are responsible for the analyses/interpretations of the data presented here. 2. Some of the data used in the municipality-level analyses are taken from NSD’s local government database (“Kommunedatabasen”). The NSD is not responsible for the analysis presented here or the interpretation offered in the BJPS-paper. 3. Note the municipality identification has been anonymized to avoid identification of individual respondents. 4. Most of the analyses generate Word-files that are produced by the outreg2 facility in STATA. These tables can be compared with those presented in the paper. The graphs are directly comparable to those in the paper. In a few cases, the results are only generated in the STATA output window. The paper employs two sets of data: I. Municipal level data in entered in STATA-format (AggregateReplicationTVData.dta), and with a corresponding data with map coordinates (muncoord.dta). The STATA code is in a do-file (ReplicationOfAggregateAnalysis.do). II. The survey data is in a STATA-file (ReplicationofIndividualLevelPanel.dta) and a with a corresponding do-file (ReplicationOfIndividualLevelAnalysis 25.08.2016.do). Please remember to change the file reference (i.e. use-statement) to execute the do-files.

In the accompanying folders, we provide data and computer code that can be used to replicate the results in our article. The "Data" folder contains various data files that serve as inputs for the analyses done in Matlab, R, and Stata. The sources of these data are explained in the article. In the "Computer Code" folder, there are three groups of programmes: i) Matlab code to execute all theoretical computations and produce various plots in the article, ii) R code to execute all statistical tests appertaining to the spatial analysis, and iii) Stata code to replicate all regression results. The "City Maps.pdf" file supplements Figure 1 in our article by showing the location of top-rated and bottom-rated restaurants across all the 96 cities in our sample.

Coded survey data prepared for processing using FCM and STATA.

Abstract

The work plan activities in Kiribati related to the updating of the listing of all households and institutions in Kiribati is to produce a sex and age disaggregated population count that forms the basis for a sampling frame for the upcoming Social Indicator Survey (SIS) and Household Income and Expenditure Survey (HIES). It also serves the purpose of digitalising and harmonising enumeration areas (EAs) to facilitate random sampling and census planning. To achieve this, SPC was engaged to conduct the following activities:

1. Planning and budgeting: prepare a comprehensive plan and budget for the household listing.
2. Mapping: prepare field maps to be used in the listing; digitalise EA boundaries and harmonisation of new EA framework; training and capacity building of the Ministry of Environment, Lands and Agricultural Development; prepare maps for the selected EAs in the SIS.
3. Listing questionnaire design, enumerator training and technology: develop a tablet-based household listing questionnaire and associated training resources, and set up of technology (e.g., server, tablet interviewer application, backup protocols); support Kiribati's National Statistics Office (KI-NSO) to conduct training of enumerators in all aspects of the collection; and administer South-South support to Kiribati for the duration of the listing.
4. Sample design: design the sample and field plan for the SIS; and build capacity of KI-NSO in sample design and field work planning.

Geographic coverage

National coverage (full coverage).

Analysis unit

Households/Institutions and Individuals.

Universe

Households, Institutions, de jure household members.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not Applicable.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire, which is designed in English, is divided into three main sections:

1) Household ID and Building Type 2) Person Roster 3) Geographic Information and Photo

The questionnaire was generated by Survey Solutions and is provided as an external resource.

Cleaning operations

Data was processed using the software STATA. Corrections were made both automatically and by visual control: validation checks in the questionnaire as well as final editing of the raw data.

Replication material for "Fundraising for Stigmatized Groups: A Text Message Donation Experiment". Please refer to README for more information. Analyses were conducted using STATA. Maps created using ArcGIS.

La Carta Geologica Interattiva del Piemonte (Progetto GeoPiemonte Map)La Carta Geologica del Piemonte è stata realizzata da CNR IGG (Istituto di Geoscienze e Georisorse, sede di Torino), ARPA Piemonte e dai Dipartimenti di Scienze della Terra e di Informatica dell'Università di Torino, con il supporto di DIATI (PoliTO) e Eni S.p.A., a conclusione di una serie di attività iniziate nel 2008. La gestione, aggiornamento dei dati, sviluppi tematici ed il mantenimento del servizio è a cura di ARPA Piemonte (responsabile del servizio e dei tematismi) e CNR IGG (responsabile dello sviluppo della base dati).Il Progetto GeoPiemonte Map è stato realizzato tramite sintesi a scala regionale di dati geologici esistenti (circa un migliaio di fonti bibliografiche consultate) ed inediti, ha portato alla realizzazione di due nuovi prodotti:- la stampa di un nuovo prodotto editoriale che rappresenta la sintesi di un progetto di ricerca durato oltre 10 anni al quale hanno contributo alcune decine di ricercatori appartenenti al CNR IGG Istituto di Geoscienze e Georisorse di Torino, all'Università di Torino - Dipartimento di Scienze della Terra, all'ARPA Piemonte e al Politecnico di Torino ¿ DIATI;- l'aggiornamento ed implementazione della Base Dati della Carta geologica del Piemonte, che si compone, attualmente di circa 10 mila informazioni ad oggi arricchita dall'interpretazione di dati di sottosuolo forniti da ENI. La Base Dati è consultabile e scaricabile dal Servizio We-bGIS del Geoportale di ARPA Piemonte. Consiste in una cartografia digitale con relativa base dati organizzata secondo modelli logici e semantici e compilata in riferimento a stan-dard descrittivi internazionali per le geoscienze (INSPIRE - Data Specification on Geology, IUGS CGI - GeoSciML Vocabulary). La prima versione della Carta Geologica del Piemonte alla scala 1:250.000 è stata pubblicata nel 2017, in formato pdf vettoriale scaricabile, sulla rivista Journal of Maps (Taylor and Francis) con una Nota illustrativa sintetica (https://www.tandfonline.com/doi/full/10.1080/17445647.2017.1316218)La seconda versione conclude il progetto di redazione grafica della Carta Geologica del Piemonte con la realizzazione in stampa tipografica di un cofanetto formato 18x25 cm circa, contenente: - copia del volume n. 41, 2017, delle Memorie dell'Accademia delle Scienze di Torino - Classe di Scienze Fisiche e Naturali, riguardante le Note Illustrative della Carta Geologica del Piemonte (Geological Map of Piemonte region at 1:250,000 scale - Explanatory Notes) di 143 pagine;- Carta Geologica del Piemonte alla scala 1:250.000 in formato tipografico A0 (quadricromia digitale) con schemi a cornice e comprendente anche una sintesi interpretata di dati di sottosuolo forniti da ENI;- Legenda bilingue (inglese-italiano) della carta geologica in formato A0 con l'elenco delle forma-zioni geologiche e delle principali fonti cartografiche consultate.La divulgazione e la vendita del cofanetto è stata affidata all'Accademia delle Scienze di Torino (https://www.accademiadellescienze.it/attivita/editoria/periodici-e-collane/memorie/fisiche/vol-41-2017) . I proventi saranno utilizzati per iniziative culturali di divulgazione della conoscenza geologica regionale e per la promozione delle attività di rilevamento geologico di studenti e dottorandi in Scienze della Terra.I prodotti fruibili sia in versione banca dati (WebGIS su Geoportale ARPA P.), sia come documenti pdf statici hanno avuto anche le seguenti finalità:a) lo sviluppo di cartografie tematiche inerenti argomenti scientifici e tecnici a partire dalla base dati e dalle geometrie della versione di base; utilizzo del prodotto a fini di divulgazione scientifica e a favore delle pubbliche amministrazioni e delle comunità professionali geo-ingegneristiche; si attendono feed-back da parte degli utilizzatori al fine di migliorare la qualità del prodotto ed incrementare la Base Dati;b) lo sviluppo di ontologie su tematiche delle geoscienze, impostate a partire dall'organizzazione logica e semantica del Data Model di progetto, in collaborazione con il Dipartimento di Informatica di UniTO. Questa attività ha portato alla definizione di "OntoGeonous", un'ontologia che ha recepito vocabolari esistenti nell'ambito delle geoscienze e che è attualmente consultabile su pagine wiki de-dicate sul sito "WikiGeo" : https://www.di.unito.it/wikigeo/index.php?title=Pagina_principaleGUIDA ALLA LETTURA DELLA CARTALa carta è consultabile e scaricabile attraverso il Servizio WebGIS del Geoportale di ARPA Piemon-te:- può essere consultata (fino alla scala di 1:70.000) interrogando le campiture colorate che indicano le unità geologiche (litostratigrafiche). Nel menù a tendina comparirà la SIGLA ed il nome dell'unità, la sua descrizione, l'età e le unità geologiche di rango superiore alla quale essa appartiene (GEOL_UNIT_1, 2, 3 ¿sintema, unità litotettonica etc¿) ed il dominio paleogeografico di riferi-mento. Infine, nella colonna LITHOLOGY è riportata una descrizione litologica sintetica, ad uso tecnico-applicativo, che esprime le caratteristiche dei litotipi in modo standard, conforme al vocabolario internazionale "Simple Lithology", per favorirne la classificazione geo-litologica o geotecnica.- può essere scaricata. I file allegati sono suddivisi in poligoni e linee con le relative proprietà ripor-tate in banca dati. Essi sono distinti in: - poligoni relativi al substrato e ai depositi quaternari;- linee relative ai contatti tettonici principali e secondari, faglie, discordanze stratigrafiche;- linee delle direttrici di conoide quaternarie;- strutture del sottosuolo (anticlinali e sinclinali; faglie ad alto angolo; sovrascorrimento; fronte tettonico sepolto);- isobate base del Pliocene ad intervallo di profondità di 250 metri.Limiti scala di rappresentazione ed impiego delle informazioni è non inferiore a 1:70.000. Ogni impiego differente da quello enunciato risulterebbe scorretto forzando lo "strumento" entro ambiti per i quali non è stato originariamente sviluppato e per i quali si declina ogni responsabilità. Per qualsiasi utilizzo in forma totale o parziale delle informazioni numeriche dovranno essere citate la provenienza e la proprietà. Per eventuali aggregazioni o rielaborazioni dei dati finalizzate alla realizzazione di prodotti diversi dall'originale, si declina ogni responsabilità, pur permanendo l'obbligo di citazione della fonte.N.B. Nella versione cartacea (pdf a scala 1:250.000) le circa 350 unità litostratigrafiche esistenti, so-no state raggruppate, per ragioni di rappresentazione grafica, in un numero minore (circa 220) di en-tità di rango superiore, corrispondenti ai singoli tasselli della Legenda grafica e ai relativi campi colo-rati. Pertanto, nel menù a tendina delle campiture del servizio WebGIS è riportato anche il campo ID_COR (ID di correlazione) corrispondente ai relativi tasselli della Legenda grafica, garantendo così l¿allineamento tra le due modalità di visualizzazione. Ne risulta che la carta interattiva del servi-zio WebGIS avrà dettaglio maggiore e potrà contenere unità litostratigrafiche distinte da sigle diver-se, ma rappresentate dallo stesso colore, attribuito in base all¿ID_COR di appartenenza.

Population Services International Zimbabwe (PSI/Z) uses commercial marketing and distribution strategies to bring health products, services and messages to vulnerable populations in urban and rural Zimbabwe. PSI/Z is implementing a multi-year USAID and DFID funded HIV prevention programs targeting sexually active Zimbabwean men and women ages 15-49 years. This MAP survey (Round 6) was conducted to assess the geographical coverage and quality of coverage of Protector Plus male condoms and CARE female condoms. The study also estimated numeric distribution of Protector Plus condoms. Total Market Approach (TMA) metrics were calculated using estimates of market volume, market value and brand diversity for Condoms in Zimbabwe. Lot Quality Assurance Sampling (LQAS) technique was used to draw a sample of 19 wards within each supervision area (SA), i.e. a district. A sample size of 19 gives reasonably accurate estimates with an acceptable error margin for decision making. A total of 92 districts and 43 high-risk areas comprising 19 growth points, 19 mining areas and 5 border towns were selected. In cases where a district had fewer than 19 wards, a census of all the wards was done and direct percentage value was calculated to determine whether the SA reaches the target coverage standard or not. Data were collected in September 2014 using Survey ToGo software on android phones. Data cleaning and analysis was done using STATA version 13.0.

IntroductionChildhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey.MethodThe current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association.ResultThe prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran’s I = 0.40, p40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6–23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting.ConclusionIn Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.

This file replicates the analysis contained in the paper. The required software is STATA (with the exception of maps figures which are run using R).

I Servizi Ecosistemici rappresentano i processi attraverso i quali gli ecosistemi naturali sostengono e soddisfano i bisogni umani; il suolo, pur non essendo di per sé un ecosistema, è stato riconosciuto come una matrice che fornisce servizi ecosistemici (Dominati at al, 2010). Essi sono suddivisi in 4 macrocategorie: Supporto, Regolazione, Approvvigionamento, Culturali (MEA, 2005, de Groot et al., 2002). Il suolo è in grado di esplicare delle funzioni molto importanti, come la regolazione del microclima, il sequestro di carbonio, la costituzione di un serbatoio di acqua, la fornitura di materie prime, cibo e fibre, habitat per i microorganismi. Nell'ambito del progetto SOS4LIFE sono state prodotte, per la parte di pianura della regione Emilia-Romagna, sei carte relative ai servizi ecosistemici BIO, BUF, WAR, WAS, CST e PRO più una carta che mostra un indice di qualità complessivo. E' stata utilizzata una metodologia appositamente messa a punto per questa area (Calzolari et al, 2016).

Questa mappa globale dell'accessibilità elenca il tempo di percorrenza via terra (in minuti) all'ospedale o alla clinica più vicini per tutte le aree tra 85 gradi di latitudine nord e 60 gradi di latitudine sud per un anno nominale 2019. Include anche il tempo di percorrenza "solo a piedi", utilizzando solo mezzi di trasporto non motorizzati. Sono in corso importanti iniziative di raccolta dei dati da parte di…

This article contain 1 table (a summary of votes outlined in the appendix), 3 figures, and an appendix. I used STATA, a commercial statistical analysis program, as well as R, a free statistics program, in this research.

I Servizi Ecosistemici rappresentano i processi attraverso i quali gli ecosistemi naturali sostengono e soddisfano i bisogni umani; il suolo, pur non essendo di per sé un ecosistema, è stato riconosciuto come una matrice che fornisce servizi ecosistemici (Dominati at al, 2010). Essi sono suddivisi in 4 macrocategorie: Supporto, Regolazione, Approvvigionamento, Culturali (MEA, 2005, de Groot et al., 2002). Il suolo è in grado di esplicare delle funzioni molto importanti, come la regolazione del microclima, il sequestro di carbonio, la costituzione di un serbatoio di acqua, la fornitura di materie prime, cibo e fibre, habitat per i microorganismi. Dopo l'esperienza del progetto SOS4LIFE (LIFE15 ENV/IT/000225), nell'ambito di una convenzione con il CNR-IBE sono state prodotte otto carte relative ai servizi ecosistemici BIO, BUF, WAR, WAS, CST, PRO, ERSPRO e BIOMASS più una carta che mostra un indice di qualità complessivo. E' stata utilizzata una metodologia appositamente messa a punto per questa area (Calzolari et al, 2016).

Background: Many meta-analyses (MAs) on Chinese medicine (CM) as an adjunctive treatment for gastric cancer have been published in recent years. However, the pooled evidence reported in MAs and their methodological quality remain unknown. Therefore, we designed a study to comprehensively evaluate and summarize the current evidence of CMs for gastric cancer in published MAs.Methods: A systematic search on MAs published in English from inception to 1st September 2021 was conducted in PubMed and Embase. The AMSTAR-2 tool was used to evaluate the methodological quality of the included MAs, and the results of the quality assessment were visualized using the evidence mapping method. Stata 17/SE was used for statistical analysis (Registration number: INPLASY202190005).Results: A total of 20 MAs (16 pairwise and 4 network MAs) were included from 118 records. These MAs were published in 14 journals from 2013 to 2021, with the number of patients and trials ranging from 688 to 6,857, and from 10 to 85, respectively. A large number of CMs (e.g., AiDi, FuFangKuShen, and HuaChanSu) in combination with chemotherapy for gastric cancer were identified among the included MAs. According to the pooled results reported in MAs, when compared to chemotherapy alone, CMs in combination with chemotherapy not only improve various outcomes on efficacy (e.g., objective response rate, quality of life) but also reduce various adverse reactions (e.g., leucopenia, nausea and vomiting). Only 2 MAs were low in terms of the overall methodological quality, while the other 18 MAs were all critically low. The methodology was required to be advanced significantly, mainly involving: study protocol and registration, explanation for the inclusion of study design, list of excluded studies with justifications, adequate details of included studies, reporting on funding sources of primary studies, and evaluation of the potential impact of risk of bias. In addition, MAs that received funds support (β = 2.68; 95%CI: 0.40 to 4.96; p = 0.024) or were published in journals with higher impact factor (β = 2.81; 95%CI: 0.69 to 4.92; p = 0.012) had a higher score on the overall methodological quality in the univariate analysis, but the results were not statistically significant according to the multivariate analysis.Conclusion: Combining CMs with chemotherapy can potentially improve clinical outcomes and reduce the relevant adverse effects in patients with gastric cancer. However, the methodological quality of relevant MAs requires significant improvement, and the current evidence needs to be validated through multinational trials that are well-designed and have a large sample size.

IntroductionChildhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey.MethodThe current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association.ResultThe prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran’s I = 0.40, p40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6–23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting.ConclusionIn Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.